HLI FORECAST SEASON 2011 - 2012 · 2015. 9. 7. · Heat Load Index Forecast Season 2013-2014 : Page...

final repport

Project code: B.FLT.

Prepared by: Andrew Wiebe, Anthony Parkinson, Christine Killip, and Ella Castillo

Katestone Environmental

Date published: May 2014

ISBN: [Request through Publications Database]

PUBLISHED BY Meat & Livestock Australia Limited Locked Bag 991 NORTH SYDNEY NSW 2059

Meat & Livestock Australia acknowledges the matching funds provided by the Australian Government to support the research and development detailed in this publication.

This publication is published by Meat & Livestock Australia Limited ABN 39 081 678 364 (MLA). Care is taken to ensure the accuracy of the information contained in this publication. However MLA cannot accept responsibility for the accuracy or completeness of the information or opinions contained in the publication. You should make your own enquiries before making decisions concerning your interests. Reproduction in whole or in part of this publication is prohibited without prior written consent of MLA.

Heat Load Index Forecast Season 2013-2014

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Contents Contents ............................................................................................................................. i Tables ... ............................................................................................................................ ii Figures .. ........................................................................................................................... iii Glossary ........................................................................................................................... iv Abstract.. ............................................................................................................................ 1 Executive Summary .................................................................................................................... 1 1. Project objectives ............................................................................................................ 2 2. Background ..................................................................................................................... 3

2.1 Global weather prediction systems ...................................................................... 3 2.2 History of the Katestone service ........................................................................... 3

3. Updates to the system for 2013/14 season ...................................................................... 5 4. Methodology .................................................................................................................... 7

4.1 The weather model .............................................................................................. 7 4.2 Heat Load ............................................................................................................ 8 4.3 Delivery .............................................................................................................. 10

5. Season overview ........................................................................................................... 12 5.1 Weather review .................................................................................................. 12 5.2 Web site statistics .............................................................................................. 12 5.3 Heat Load Events .............................................................................................. 14 5.4 Forecast performance evaluation ....................................................................... 15

5.4.1 Evaluation parameters ............................................................................ 16 5.4.2 Benchmark locations .............................................................................. 18

Table 6Performance benchmarks by site 1 day ahead .............................................................. 21 Table 8Performance benchmarks by site 3 days ahead ............................................................ 23 Table 9Performance benchmarks by site 4 days ahead ............................................................ 24

5.5 Subscriber survey .............................................................................................. 27 5.5.1 User Profiles ........................................................................................... 27 5.5.2 Usage ..................................................................................................... 27 5.5.3 Ease of Use ............................................................................................ 27 5.5.4 Alerts ...................................................................................................... 28 5.5.5 Performance ........................................................................................... 29

6. Discussion ..................................................................................................................... 30 6.1 Future improvements ......................................................................................... 30

7. Conclusion .................................................................................................................... 31 8. Bibliography .................................................................................................................. 32 Appendix A AHLU Performance ............................................................................................ 33

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A1. Day 1 ................................................................................................................. 33 A2. Day 2 ................................................................................................................. 50 A3. Day 3 ................................................................................................................. 67 A4. Day 4 ................................................................................................................. 84

Appendix B Survey Results ................................................................................................. 101 B1. General Information ......................................................................................... 101 B2. Registered Subscriber ..................................................................................... 103

B1. User Administration .............................................................................. 103 B2. Forecast Accuracy ................................................................................ 106 B3. Heat Load Data Network (HLDN) ......................................................... 108 B4. Alerts .................................................................................................... 109

B3. Unregistered respondents ................................................................................ 113

Tables Table 1 Southern Oscillation Index 30 day rolling average ................................................ 12 Table 2 Web site traffic for 2012-13 season and 2013-14 season ..................................... 12 Table 3 Heat Load Advisories issued between October 2013 and March 2014 ................. 14 Table 4 Performance benchmark guidelines ...................................................................... 18 Table 5 Forecast benchmark locations .............................................................................. 18 Table 6 Performance benchmarks by site 1 day ahead ..................................................... 21 Table 7 Performance benchmarks by site 2 days ahead .................................................... 22 Table 8 Performance benchmarks by site 3 days ahead .................................................... 23 Table 9 Performance benchmarks by site 4 days ahead .................................................... 24 Table 10 1 and 2 day forecast AHLU contingency tables for all locations for the forecast

season (forecast is on the horizontal and observed on the vertical) ...................... 25 Table 11 3 and 4 day forecast AHLU contingency tables for all locations for the forecast

season .................................................................................................................. 26

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Figures

Figure 1 Home page for the Cattle Heat Load Toolbox ......................................................... 2 Figure 2 Up take of the CHLT service since its launch in 2010/11 ........................................ 4 Figure 3 My Site summary page for registered users ............................................................ 6 Figure 4 K-WRF forecast domain .......................................................................................... 7 Figure 5 Schematic of the land surface scheme used in the K-WRF system ......................... 8 Figure 6 Schematic of the Cattle Heat Load Toolbox generation and delivery system ........ 10 Figure 7 Web site visits as a function of time of the day ...................................................... 13 Figure 8 Web site visits as a function of day of the week .................................................... 13 Figure 9 Web site visits as a function of State ..................................................................... 14 Figure 10 Breakdown of alerts sent by month during forecast period .................................... 15 Figure 11 Emerald, QLD observed and predicted AHLU 86 for the period January 2 – 7

2014 ..................................................................................................................... 16 Figure 12 Benchmark locations............................................................................................. 19 Figure 13 Mean HLI RMSE for the 17 benchmark sites since 2006. Solid line denotes the

beginning of K-WRF operational forecast. Dashed line denotes the update in the GFS model physics and resolution. Dotted line denotes the introduction of the SRTM 3 arc second terrain dataset to the K-WRF system .............................. 20

Heat Load Index Forecast Season 2013 - 2014

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Glossary Term Definition ACCESS Australian Community Climate and Earth-System Simulator AFWA Air Force Weather Agency AHLU Accumulated Heat Load Unit ARW Advanced Research and Weather AWS Automatic Weather Stations BOM Bureau of Meteorology CAPS Center for Analysis and Prediction of Storms CHTL Cattle Heat Load Toolbox DFI Digital Filter Initialisation ECMWF European Centre for Medium-Range Weather Forecasts ENSO The El Niño/Southern Oscillation ESRL Earth System Research Laboratory FAA Federal Aviation Administration GASP Global Analysis and Prediction model GFS Global Forecasting System HLI Heat Load Index HPC High performance computing IOA Index of Agreement km kilometre K-WRF Katestone Weather Research and Forecasting Model LAPS Local Area Prediction System LSM Land Surface Mocdel M metre m/s metres per second MAE mean absolute error MJO Madden-Julian Oscillation MMM Mesoscale and Microscale Meteorology MOS Model output statistic NCAR National Centre for Atmospheric Research NCEP National Centre for Environmental Prediction NMM Non-Hydrostatic Mesoscale Model NOAA National Oceanic and Atmospheric Administration NRL Naval Research Laboratory NWP Numerical weather prediction NWP Numerical weather prediction ºC degrees Celsius RMSE root mean squared error SOI Southern Oscillation Index SRTM Shuttle Radar Tomography Mission SST sea surface temperature TC Tropical Cyclones VAR variational data-assimilation WMO World Meteorological Organisation WPS WRF Preprocessing System WRF Weather Research and Forecasting WSF WRF System Framework


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Abstract

Heat stress in feedlot cattle can have a deleterious effect on cattle performance and in extreme cases lead to cattle death. The National Feedlot Accreditation Scheme requires that feedlots have a heat stress management plan in place to cope with weather events that can lead to excessive heat loads. The Cattle Heat Load Toolbox, developed by Katestone, alerts feedlot operators of impending adverse weather conditions that could lead to excessive heat loads in feedlot cattle. The toolbox is web based and provides access to weather and heat load forecasts out one week and risk assessment programs. The service is underpinned by over 10 years of research into cattle heat load funded by MLA. The Cattle Heat Load Toolbox brings all this research together and uses a world class weather forecasting system to generate accurate forecasts across Australia. This service provides useful and practical information to help feedlot operators manage heat stress in cattle through advanced warning of adverse conditions. Thus allowing operators time to undertake appropriate actions to mitigate the risk of heat stress when alerted. Executive Summary

Management of heat stress in feedlot cattle is required as part of the National Feedlot Accreditation Scheme The Cattle Heat Load Toolbox provides an early warning system for impending adverse weather conditions that could lead to excessive heat loads in feedlot cattle. The toolbox is web based and provides access to weather and heat load forecasts out one week and risk assessment programs. Feedlot Operators subscribe to the service free of charge and request a forecast for their feedlot. Subscribers also define risk alert levels suitable to their feedlot management and cattle type and condition through the Risk Assessment Program. Alerts are sent daily by email or SMS to designated recipients (e.g. site managers, veterinarians). Under severe heat load conditions a written heat load advisory detailing the location, cause and likely duration of a heat load event is issued by Katestone meteorologists. The Advisory is emailed to all subscribers and posted on the toolbox web site. There are currently 241 subscribers and 150 feedlots registered to the forecasting service, covering nearly a million head of feedlot cattle across Australia. Katestone provides weather and heat load forecasts for the 241 locations, comprising of 150 registered sites and 91 public sites. The forecast service runs 24 hours a day 7 days a week, providing hourly data for four days into the futures with an extended daily outlook to 7 days. Katestone has been providing this service for over ten years and in that time we have extended the service from 16 to 241 forecast locations. The forecast for the service is generated by our in-house high performance computer using the Weather Research & Forecasting model. The system is monitored by Katestone meteorologists, scientists and engineers throughout the summer season and assessed for performance in predicting the location, magnitude and duration of heat load events. The system has proven to accurately predict these key features and alert the relevant operators of the impending situation.


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1. Project objectives

The Cattle Heat Load Toolbox (see Figure 1) was developed to assist in warning feedlot operators of impending adverse weather conditions that could lead to excessive heat loads (and potential mortality) for feedlot cattle. The objective of the project is to:

• Provide heat load forecasts for feedlot operators across Australia

• Deliver warnings of impending heat load events

• Provide access to forecasts and the Risk Analysis Program over the internet

• Provide guidance to feedlot operators to minimise losses due to heat load

Figure 1 Home page for the Cattle Heat Load Toolbox


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2. Background

Katestone has provided a heat load prediction service to the Australian Lot Feeders Association since 2003. During this time the technology involved in delivering the service has changed dramatically. From the data used to initialise the model, the underlying equations for estimating heat load, to the delivery of the forecast on the Cattle Heat Load Toolbox website.

2.1 Global weather prediction systems

There are currently three major providers of Global weather forecasts, the NCEP/NOAA of the USA, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Met Office Hadley Centre. These centres run global circulation models every 6 hours for up to 30 days into the future. They take all the data available from the WMO to initialise the state of the atmosphere. These models range in horizontal resolution over Australia from 25 km to 60 km and provide data at three hourly time steps. These models are:

• The Global Forecasting System (GFS) from NCEP/NOAA • The Global Atmospheric Model from ECMWF • The Unified Model from the UK Met Office Hadley Centre

Of these only the GFS is made publicly available. The other forecast systems are only available under commercial licences in a cost structure that would be prohibitively expensive for the magnitude of the Cattle Heat Load Toolbox system.

The Bureau of Meteorology (BOM) has its own modelling system known as ACCESS, the Australian Community Climate and Earth System Simulator. ACCESS is a derivative of the Unified Model and has replaced the old GASP, LAPS and MesoLAPS models.

ACCESS provides forecasts at several resolutions from Global at 80 km to city specific at 5 km. In between is ACCESS-A which covers the Australian region and is at a resolution of 12 km. ACCESS-A forecasts extend 48 hours into the future and data is provided at 3 hourly intervals. Again these data streams are available at a cost, while at a marginal rate compared the European systems.

The reality is that none of these systems meet the requirements of predicting heat load in feedlot cattle. They are to coarse in their resolution, where significant variations in terrain are missed or they do not extend far enough into the future to provide adequate early warning to feedlot operators and they do not provide an hourly data set from which to calculate a the AHLU.

2.2 History of the Katestone service

When Katestone started investigating the development of a heat load forecast service in 2003 this issue was largely the same. None of the available forecast streams met the needs of the modern Australian Feedlot. In response to this Katestone developed a statistical modelling solution that incorporated the GASP and LAPS models from the Bureau of Meteorology and downscaled the data where local automatic weather stations (AWS) were available.

This system worked well at the time as computer simulations where in their infancy and the sole domain of governments and military installations. The technology required to produce weather forecasts has progressed since then, while still specialised and infrastructure


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intensive the ability to provide numerical weather predictions (NWP) that meet the needs of lot feeders across Australia has arrived.

It was known that the statistical program was limited to the input data, 91 sites that coincided with BOM AWS locations and that the dynamic interactions between temperature, relative humidity and wind speed were neglected, such as when temperature increases, relative humidity must decrease. In this respect the system was missing some key features of the weather system. But more importantly the system was very limited spatially and did not cover large areas where feedlots actually exist.

To address these issues Katestone invested significant amounts of capital and research and development to establish a computing facility capable of delivering numerical weather predictions (NWP) at an hourly interval for 4 days into the future for any location in Australia. Katestone tested this new system in parallel with the statistical system for the 2009-2010 forecast season and found that the NWP system performed better than statistical system in forecasting heat load and the meteorological variables that feed into its calculation.

The 2010-2011 forecast season saw the launch of site specific forecasts for registered users. The registered feedlot could request a forecast for their specific location and were no longer reliant on forecasts for locations that were at times over 100 km away. A total of 25 users registered for this service bringing the total number of sites with hourly 4 day forecasts to 116.

The following forecast season, 2011-2012, Katestone launched the Cattle Heat Load Toolbox (CHLT) website. In addition to site specific forecasts, an email and SMS alert system was implemented. The Toolbox also provided more information about the meteorological variables that go into calculating the HLI and the AHLU and how they change throughout the day.

There are now 238 subscribers and 150 feedlots registered (Figure 2) to the forecasting service covering nearly a million head of feedlot cattle across Australia; forecast data are also provided for 91 publicly available sites. The forecast service runs 24 hours a day 7 days a week, providing hourly data for four days into the futures with an extended daily outlook to 7 days, and is continuously monitored by one of our scientists and engineers.

Figure 2 Up take of the CHLT service since its launch in 2010/11


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3. Updates to the system for 2013/14 season

The Cattle Heat Load Toolbox (CHLT) has moved to a new home (http://chlt.katestone.com.au). The website is now hosted by a remote server controlled by Katestone, providing a higher level of customer service and functionality to the feedlot industry. New features that were implemented this year include:

• Forecast summary page for registered feedlot sites providing (see Figure 3): o Simplified data to daily summaries o Display of critical information quickly and intuitively o Easily navigation to detailed forecasts and observations o Display of recent alerts and current settings

• Online management of accounts to allow Feedlot Site Managers to: o Add and remove users to their site o Set up and change AHLU alert levels for any of their users o Change contact details o All changes are updated instantly in the online database

• Online management of accounts to allow users added to a site by their manager to: o Change AHLU alert levels o Change contact details o Be added to multiple sites (handy for vets and nutritionists)

• Written Heat Load Advisories o Katestone monitors the development of heat load events daily o These alerts are in addition to the AHLU threshold alerts set by the user o The written alert identifies key drivers and regions that are likely to be

affected o These alerts cover aspects of heat load that are not catered for in the AHLU

threshold alerts; such as rapid changes in the HLI following periods of intense rain

• Seasonal outlooks o Updates on ENSO fluctuations o Projected deviations from long term averages for rain and temperature

• The Heat Load Data Network was established to allow integration of feedlot site weather stations into the CHLT to provide:

o Online display of feedlots weather station data on the My Site Observation pageInitialising the AHLU forecast with calculated AHLU from site AWS data

o Quality checking of AWS data before calculating the HLI and AHLU The Heat Load Data Network (HLDN) has been one of the most important additions to the CHLT service this season. The HLDN provides online access to the data and integration of the observed AHLU into the forecast. Katestone provides a over the phone advice and development of upload programs to connect a feedlot AWS to the network. This also includes a data quality check prior to integration and display. To date a total of 14 registered feedlots are connected to the HLDN with many more in progress. As of the 2013/14 season Katestone will continue to maintain and update the CHLT service year round. The forecast will be generated daily and email only alerts issued. The SMS and Heat Load Advisory service will only be operational from November to March. This cool period is essential to maintain and update the system with the latest algorithms and datasets to continuously improve the forecast performance and provide the best possible service the feedlot industry.

http://chlt.katestone.com.au/


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Figure 3 My Site summary page for registered users


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4. Methodology

4.1 The weather model

The Weather Research and Forecasting – Advanced Research and Weather (WRF-ARW) model, or simply WRF is a numerical weather prediction (NWP) system used the world over by national weather services and militaries for mission critical applications. Katestone’s implementation of the WRF model (K-WRF) is initialised twice daily producing a 4 days at an hourly time step. The modelling domain extends from 105°E to 160°E and 8°S to 45°S encompassing a significant portion of the oceans to better resolve the generation of tropical weather systems (Figure 4). The resolution of the model is 12 km, meaning that data is generated at 12 km spacing over a 23,436,000 km2 area. K-WRF receives its initial conditions from the GFS model, which already contains data assimilated from the Australian WMO monitoring sites. The model also incorporates a detailed land surface model that accounts for soil type, moisture content, porosity and vegetation type and density (Figure 5).

Figure 4 K-WRF forecast domain


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Figure 5 Schematic of the land surface scheme used in the K-WRF system 4.2 Heat Load

There are a number of climatic conditions that may predispose feedlot cattle to high body heat loads, including:

• A recent rain event • A high ongoing minimum and maximum ambient temperature • A high ongoing relative humidity • An absence of cloud cover with a high solar radiation level • Minimal air movement over an extended period (4-5 days) • A sudden change to adverse climatic conditions

It is usually a combination of some or all of these conditions that leads to an excessive heat load event, which may result in cattle deaths if conditions persist for a number of days.

The calculation of HLI requires Relative Humidity (RH) expressed as a percentage, Wind Speed (WS) in m/s and Black Globe Temperature (BGT) in °C. HLI is calculated as a composite of HLILOW and HLIHIGH, with a weighting factor determined as a function of the difference in the calculated BGT and a threshold of 25 °C. A blending function was introduced as a result of an analysis of data over time, wherein it was evident that large jumps in HLI could occur under the some circumstances when the BGT passes through 25°C – for example from 24.9°C to 25.1°C.

In equation form, HLILOW and HLIHIGH are calculated as follows, noting that exp is the exponentiation function:

• HLILOW = 1.3 x BGT + 0.28 x RH - WS + 10.66 • HLIHIGH = 1.55 x BGT + 0.38 x RH - 0.5 x WS + exp (2.4 - WS) + 8.62

The weighting factor is calculated and used as:


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• FRACTIONHIGH = 1.0 / (1.0 + exp(-((BGT-25.0)/2.25))) • HLI = (FRACTIONHIGH * HLIHIGH) + ((1 – FRACTIONHIGH) * HLILOW)

It is also worth noting that if any calculation of HLI yields a value less than 50, this value should be set to 50, as the dissipation of heat does not increase below this point.

The use of BGT in calculating the HLI, rather than ambient temperature, takes into account radiation effects as well as air temperature. Although sensors for measuring BGT exist, these are not included as part of the standard weather station and must be ordered from a suitable supplier. In the absence of measured BGT, a quantified relationship between BGT, ambient temperature (T) and solar radiation (SR) can be used. Here solar radiation can either be a measured value or a calculated value.

BGT can be calculated from T and SR using the following equation (noting that log is the logarithm function using base-10):

• BGT = 1.33 x T - 2.65 x √T + 3.21 x log (SR + 1) + 3.5

Accumulated Heat Load Units (AHLU) has been developed to give some indication of the amount of heat that is accumulated by an animal when it is exposed to environmental conditions that are above its ability to maintain thermoneutral conditions. For every hour that an animal is above its threshold HLI value, it will gain heat. This additional heat load accumulates over time and is reflected as an increase in body temperature. It is a normal physiological response for animals to gain heat during the day and dissipate this accumulated heat to the environment at night. If the animal cannot dissipate this accumulated heat overnight, the animal carries a heat load into the following day. This makes the animal more susceptible to the effects of subsequent heat load. The three aspects that determine the potential for excessive heat load in feedlot cattle include time, intensity, and the opportunity to dissipate heat.

In order to calculate the AHLU, the HLI, upper (UL) and lower (LL) limit of the thermal neutral zones, and interval (in hours) between successive HLI estimates (Int) are required. LL is fixed at 77, while UL is a variable dependent on the HLI value at which stock begins to accumulate heat. This depends on the stock characteristics, location, and management practices including mitigation measures. The equation for calculating AHLU is as follows:

• AHLUCURRENT = AHLUPREVIOUS + BALANCE If the HLI is less than LL (77), then the heat is dissipated at half the rate of accumulation (the difference between HLI and LL). If the HLI falls between the LL and UL, then heat is neither dissipated nor accumulated. If the HLI is greater than UL, heat is accumulated. In equation form, the AHLUCURRENT is calculated as:

• HLI <= 77 AHLUCURRENT = AHLUPREVIOUS – Int * (77 - HLI) / 2 • 77 < HLI < UL AHLUCURRENT = AHLUPREVIOUS • HLI >= UL AHLUCURRENT = AHLUPREVIOUS + Int * (HLI – UL)

AHLU values do not go below zero. If any calculation results in an AHLU value below zero, it is set to zero.

http://chlt.katestone.com.au/wp-admin/post.php?post=1164&action=edit#bgt-def


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Sites connected to the HLDN are initialised from AHLU calculated from data collected at local AWS, which theoretically would result in a more accurate AHLU forecast. The same holds true for BOM sites. Sites which do not have an integrated AWS are initialised from the previous day’s AHLU forecast. 4.3 Delivery

The predicted HLI and AHLU levels are delivered to subscribers through several channels, these include:

• Display on the CHLT website (chlt.katestone.com.au) • Automated AHLU threshold Email and SMS alerts • Written Heat Load Advisories

The forecast is updated and produced every 12 hours (twice daily) during the summer season, and once a day the rest of the year. Once the forecast is complete the relevant information is extracted for the public forecast sites and the registered users and processed to calculate the HLI and AHLUs. The forecasts are uploaded to the website at approximately 6 AM and 6PM daily, automated alerts are only sent from the 6 AM forecast. The CHLT website provides forecast weather and heat load information to users for a seven day period. The first three days are derived from hourly forecast generated by the K-WRF modelling system and the final three days are extracted from the Global Forecasting System (GFS). Figure 6 is a schematic of the entire system from data acquisition to forecast generation to delivery.

Figure 6 Schematic of the Cattle Heat Load Toolbox generation and delivery system

http://chlt.katestone.com.au/


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Subscribed feedlot operators specify AHLU thresholds that triggered a warning email and/or SMS to nominated recipients for sites that are forecasted to experience adverse conditions. Operators can also nominate several sites to monitor; this option remains popular with regional veterinarian services that some feedlot operators nominated as recipients. Under extreme situations that have the potential to adversely affect a significant number of locations a Heat Load advisory is issued by a Katestone meteorologist.

As part of the operational procedures for supplying the forecast a daily checklist is completed by a Katestone meteorologist. These checks include but are not limited to:

• K-WRF, GFS, ACCESS, ECMWF comparison • Weather systems analysis and comparison to satellite observations • Hot spot identification and analysis • Performance analysis versus observations (magnitude and timing) • Heat Load Advisory alerts when required • Error creep in the AHLU from false AWS records

The CHLT website is administered and maintained by a Katestone system administrator. The administrator registers new subscribers; checks their coordinates are valid, configures their site specific user interface and sets the AHLU alert thresholds and delivery accounts as per the subscriber’s instructions. The administrator also has a daily check list that includes:

• K-WRF simulation completed, processed and uploaded to the web • Check Email and SMS alerts sent • Update subscribers list if required • Web site maintenance and analytics

Katestone provides guidance over the phone to operators who wish to gain a better understanding of the situation and have provided site specific analysis of feedlots onsite AWS data when requested. These services are outside the scope of work defined for this project but are supplied for the operational benefit of the end users.


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5. Season overview

5.1 Weather review

The Southern Oscillation Index (SOI) remained neutral over the 2013/14 summer season (Table 1). This summer was similar to the 2012/13 summer that was punctuated by swings from very hot and dry conditions to warm and wet, interspersed with Tropical Cyclones and localised flooding. Ten tropical cyclones were recorded in the Australian region, four impacting Western Australia and the Northern territory and six impacting Queensland and the Northern Territory.

Table 1 Southern Oscillation Index 30 day rolling average

Date Oct-13 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 SOI -1.9 9.2 0.6 12.2 -1.3 -13.3 Source: Bureau of Meteorology The beginning of January was characterised by extremely high temperatures in central and eastern Australia with parts of southern inland Queensland experiencing maximum temperatures 8°C above average for the week ending January 4. Similarly, Southeast Australia experienced extreme temperatures between January 13 and 18, with parts of Victoria recording maximum temperatures 12°C above average for that week. According the Bureau of Meteorology (BOM) the summer of 2013/14 was warmer than average for both maximum and minimum temperatures for the Southern half of Australia and extending into Queensland. Nationally the summer rainfall was 15% above average; however this is split between below average rains for Eastern Australia and above average rains for Western Australia. 5.2 Web site statistics

The web site was well visited with 8,500 sessions from over 1,600 users over the six month period October to March. The traffic has increased by almost 20% from the same period last year, with 30% more users (See Table 2).

Table 2 Web site traffic for 2012-13 season and 2013-14 season Season Page

Views Site

Users Sessions Mobiles

Sessions Average time on site

(mins) 2012-13 21,784 1,296 7,143 161 2.38 2013-14 28,350 1,683 8,515 524 2.57 The following tables present the breakdown of the web site visits as a function of time of day (Figure 7) and day of the week (Figure 8) and State (Figure 9). The time of day and day of week breakdowns indicate that the site is most frequently used in the morning 6-10 am on week days. The afternoon run of the forecast (uploaded about 5pm) is almost never looked at. Queensland is by far the most active state with 60% of visits.


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Figure 7 Web site visits as a function of time of the day

Figure 8 Web site visits as a function of day of the week


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Figure 9 Web site visits as a function of State 5.3 Heat Load Events

Katestone issued Heat Load Advisories when heat load events where likely to impact a region and persist for several days. 15 advisories where issued between October 2013 and March 2014. These are summarised in Table 3 detailing the issue date, projected date of peak heat load and regions affected.

Table 3 Heat Load Advisories issued between October 2013 and March 2014

Date Issued Peak heat event Regions Type 1/10/2013 3/10/2013 QLD central, SEQ Heat Load 11/11/2013 14/11/2013 QLD central, SEQ Heat Load 22/11/2013 17/11/2013 QLD central, SEQ Rapid change in HLI 16/12/2013 19/12/2013 WA, VIC, Riverina Heat Load 27/12/2013 31/12/2013 SEQ, Central QLD, Northern NSW Heat Load 30/12/2014 4/01/2014 SEQ, Central QLD, Northern NSW Heat Load 3/01/2014 5/01/2014 SEQ, Central QLD, Northern NSW Heat Load 13/01/2014 17/01/2014 SA,Vic, Western NSW Heat Load 23/01/2014 26/01/2014 Northern NSW, SEQ Heat Load 29/01/2014 1/02/2014 NSW,SA,VIC,WA Heat Load 30/01/2014 1/02/2014 Central QLD Rapid change in HLI 4/02/2014 6/02/2014 WA Heat Load 17/02/2014 20/02/2014 NSW and QLD Heat Load 21/03/2014 23/02/2014 NSW and QLD Heat Load 27/03/2014 28/03/2014 NSW and QLD Rapid change in HLI


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A total of 3,177 emails and 1,788 SMS alerts were issue during the 2013/14 summer forecast period, with a peak number of alerts sent in January (Figure 10).

Figure 10 Breakdown of alerts sent by month during forecast period

5.4 Forecast performance evaluation

The weather systems that drove the major heat events were simulated accurately by the model and communicated to subscribers as soon possible. Overall the system performed well in predicting the meteorological parameters that are used as inputs for the HLI. Major heat load events and rapid changes in the HLI where captured by the K-WRF system and communicated to operators by the automated alerts system and the written Heat Load Advisory service and warnings were issued at least 3 to 4 days in advance and at times 7 days in advance. For example Emerald, QLD experienced a prolonged heat load event between January 3 and January 7 2014 with a peak Heat Load event occurring on January 5. A Heat Load Advisory was issued on December 27 2013 and followed the progress of the situation with updates on December 30 2013 and January 3 2014. The automated AHLU 86 medium threshold alert was identified 4 days out from the forecast issue date and continued to predict the progress of the event (Figure 11).

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Figure 11 Emerald, QLD observed and predicted AHLU 86 for the period January 2 –

7 2014 5.4.1 Evaluation parameters

The measures used to evaluate performance are the Index of Agreement (IOA), the Mean Absolute Error (MAE), Bias and the Root Mean Square Error (RMSE). For example:

• an IOA of 1 implies a perfect forecast with no difference between the observed and the predicted at all points in time

• an MAE of 2 for temperature signifies an average error of 2°C over the entire period of the analysis

• a Bias of -1 for temperature indicates that on average the model under predicts the temperature over the entire period of the analysis

• a RMSE of 2 for temperature signifies an average squared error of 2°C, the statistic exposes large errors and should be considered in relation to the MAE

• the closer the RMSE is the to the MAE indicates that most of the errors of the same magnitude and that they are consistent, this is preferable.

The IOA is the ratio of the total RMSE to the sum of two differences: the difference between each prediction and the observed mean and the difference between each observation and observed mean. The IOA is a good measure of model performance in that it compares each individual hour against the mean of the observations, such that if the predicted and observed values vary at different scales or rates a poor agreement will result (< 0.6). Where An IOA of 1 indicates perfect agreement or both the predicted and observed vary about the mean at the same scale and rate. The equation for the IOA is:


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The root mean squared error (RMSE) can be described as the standard deviation of the difference for hourly prediction and observation pairings at a specific point which measures the average magnitude of the error. The difference between forecast and corresponding observed values are each squared and then averaged over the sample. Finally, the square root of the average is taken. Since the errors are squared before they are averaged, the RMSE gives a relatively high weight to large errors. This means the RMSE is most useful when large errors are particularly undesirable. Overall, the RSME is a good overall measure of model performance. The equation for the RMSE is:

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1)(1RMSE ∑

=

−=N

iii OP

N

The mean absolute error (MAE) measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average. The equation for the MAE is:

)(MAE1

OP i

N

iiabs −=∑

−

The MAE and the RMSE can be used together to diagnose the variation in the errors in a set of forecasts. The RMSE will always be larger or equal to the MAE; the greater difference between them, the greater the variance in the individual errors in the sample. If the RMSE=MAE, then all the errors are of the same magnitude. The Bias is simply the mean difference between the observed and the predicted. It provides a measure of the average sign and magnitude of the error; such that a negative value is an under prediction and a positive value an over prediction. The equation for Bias is:

)(BIAS1

OP i

N

ii −=∑

−

There are no defined standards for NWP model performance; statistical scores simply provide a means to quantify the magnitude of the difference between predictions and observations. Table 4 is a useful guide to performance benchmarks of what should be expected from a model. These values are guidelines and not absolute values of pass or fail. They are based on hind cast model evaluation techniques; where a hind cast is initialised by re-analysis data and corrected observations are assimilated (Emery et al 2001).


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Table 4 Performance benchmark guidelines

Measure HLI T RH WS IOA >0.9 >0.7 >0.7 >0.6 MAE <6 <2 <10 <2 BIAS ±2 ±1 ±5 ±1 RMSE <6 <2 <15 <2 5.4.2 Benchmark locations

This year the performance of the forecasting service has been assessed against 17 benchmark locations. Most of these sites have been included in the forecast service since its inception and provide a good measure of the forecasts performance over the years (Figure 12). Table 5 describes the benchmark locations.

Table 5 Forecast benchmark locations

Site Name Lat Lon WMO code State Hay -34.54 144.83 94702 NSW Moree -29.48 149.84 95527 NSW Griffith -34.24 146.06 95704 NSW Yanco -34.62 146.43 95705 NSW Tamworth -31.07 150.83 95762 NSW Cessnock -32.78 151.33 95771 NSW Armidale -30.52 151.61 95773 NSW Albury Airport -36.07 146.95 95896 NSW Emerald -23.56 148.17 94363 QLD Roma -26.54 148.77 94515 QLD Oakey -27.4 151.74 94552 QLD Warwick -28.2 152.1 94555 QLD RAAF Amberley -27.62 152.71 94568 QLD Miles -26.65 150.18 95529 QLD Clare High School -33.82 138.59 95667 SA Charlton -36.28 143.33 94839 VIC Katanning -33.68 117.6 94641 WA


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Figure 12 Benchmark locations Figure 13 shows the progression of the forecasts performance since 2006 for the 17 benchmark locations. The solid line in Figure 13 marks the period where the K-WRF system became the operational forecast engine for the CHLT service. The first two years of operations saw a significant improvement in the overall performance. The 2012/2103 season was not as accurate as the previous year, this is likely due to changes in the GFS model that initialises the K-WRF model. Katestone identified this issue as a discrepancy between the GFS terrain, physics and resolution and that of K-WRF. During the off season Katestone updated K-WRF’s terrain dataset with the SRTM 3 arc second (90 metre) digital elevation model. This is shown in Figure 13 as the dotted line and by the marked improvement in the forecast performance for the 2013/2014 summer season.


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Figure 13 Mean HLI RMSE for the 17 benchmark sites since 2006. Solid line denotes

the beginning of K-WRF operational forecast. Dashed line denotes the update in the GFS model physics and resolution. Dotted line denotes the introduction of the SRTM 3 arc second terrain dataset to the K-WRF system

Table 6, 6, 7 and 8 show the 1, 2, 3 and 4 day forecast performance for HLI, temperature, relative humidity and wind speed for the 17 benchmark locations. The forecast maintains the same level of accuracy out to 3 days with only a slight decrease by day 4. Table 10 shows the 1, 2 and 3 day forecast AHLU contingency tables for all locations combined for the forecast season. Appendix A contains AHLU contingency tables for the benchmark forecast locations. In the contingency tables, the horizontal represents the forecast and the vertical represents the observed AHLU category. The value in the table represents the percentage of time the forecast accurately predicted the AHLU risk level, where 100 is a perfect score. The underlying data is hourly therefore to interpret the frequency of the error one must relate the percentage to the number of hours in the analysis.


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Table 6 Performance benchmarks by site 1 day ahead HLI Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.97 0.97 0.97 0.97 0.97 0.97 0.96 0.97 0.96 0.97 0.97 0.97 0.97 0.98 0.96 0.98 0.97 MAE 3.04 2.93 3.16 2.56 2.77 2.79 2.89 2.1 3.19 2.44 2.4 2.81 2.9 2.36 2.44 1.82 2.4 BIAS 0.17 0.63 1.96 0.2 0.47 1.44 1.48 0.23 -0.22 -0.18 0.4 -0.32 -1.11 0.96 1.19 -0.6 1.23 RMSE 4.51 4.38 4.51 3.84 4.07 4.23 4.6 3.32 4.55 3.98 3.87 4.41 4.21 3.72 4.11 2.98 3.96

BGT Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.98 0.97 0.96 0.97 0.97 0.98 0.98 0.97 0.97 0.98 0.98 0.97 0.97 0.98 0.98 0.99 0.98 MAE 1.86 1.99 2.87 2.23 2.66 2.08 2.39 2.19 2.7 2.06 2.58 2.38 2.7 2.07 2.17 1.73 2.52 BIAS 1.14 0.83 1.91 1.56 1.43 1.58 0.62 1.61 2.16 0.72 0.85 1.48 2.06 1.21 -0.17 0.72 1.19 RMSE 2.41 2.63 3.39 2.81 3.19 2.61 2.88 2.82 3.27 2.62 3.13 2.91 3.24 2.59 2.98 2.39 3.17

T Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.96 0.95 0.92 0.95 0.94 0.96 0.97 0.95 0.93 0.98 0.97 0.95 0.94 0.98 0.98 0.98 0.97 MAE 1.54 1.58 2.45 1.82 2.26 1.7 1.98 1.82 2.25 1.74 2.11 1.98 2.33 1.72 1.62 1.3 2.01 BIAS 0.79 0.5 1.53 1.16 1.08 1.17 0.27 1.17 1.63 0.46 0.54 1.1 1.63 0.92 -0.48 0.41 0.8 RMSE 1.97 2.05 2.87 2.23 2.71 2.1 2.4 2.32 2.7 2.17 2.56 2.42 2.78 2.1 2.24 1.72 2.47

RH Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.92 0.93 0.88 0.92 0.88 0.92 0.9 0.91 0.85 0.93 0.93 0.89 0.86 0.94 0.93 0.93 0.95 MAE 8.68 8.57 12.56 10.14 11.75 9.61 9.52 10.59 13.81 8.6 8.04 11.33 13.76 7.65 8.7 10.45 7.77 BIAS -4.32 0.64 -5.86 -6.41 -2.99 -6.05 4.05 -3.63 -9.86 -1.45 -1.62 -6.19 -10.74 -1.57 4.71 -7.95 -3.29 RMSE 11.4 12.02 15.57 12.73 15.78 12.27 12.93 14.72 16.61 11.48 10.81 14.52 16.17 10.43 12.8 12.87 10.14

WS Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.82 0.78 0.58 0.8 0.74 0.65 0.72 0.79 0.74 0.8 0.82 0.78 0.73 0.83 0.61 0.88 0.77 MAE 1.48 1.32 2.24 1.57 1.52 2.27 1.49 1.43 1.3 1.47 1.44 1.55 1.68 1.25 2.4 1.12 1.56 BIAS -0.06 -0.04 -1.99 -0.33 -0.03 -1.72 -0.31 0.64 0.22 0.32 -0.1 0.48 0.87 -0.43 -2.17 -0.56 -1.13 RMSE 1.93 1.8 2.73 2.1 2.04 2.78 1.9 1.82 1.66 1.91 1.93 2.07 2.15 1.65 2.86 1.43 1.96


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Table 7 Performance benchmarks by site 2 days ahead HLI Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.96 0.96 0.96 0.97 0.96 0.97 0.97 0.96 0.96 0.97 0.97 0.97 0.97 0.97 0.96 0.98 0.97 MAE 3.11 3.33 3.52 2.67 3.12 3.03 2.81 2.27 3.33 2.57 2.67 3 2.9 2.52 2.55 1.94 2.43 BIAS 0.34 1.06 2.42 0.5 1.2 1.66 1.09 0.54 -0.22 -0.31 0.26 0.16 -0.85 0.85 1.16 -0.66 0.97 RMSE 4.58 5.04 5.12 4.04 4.67 4.4 4.37 3.55 4.63 4.08 4.15 4.61 4.27 3.9 4.29 3.15 3.89

BGT Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.98 0.98 0.96 0.97 0.96 0.98 0.98 0.97 0.96 0.98 0.97 0.97 0.97 0.98 0.98 0.98 0.98 MAE 1.85 1.86 2.82 2.28 2.76 2.15 2.48 2.34 2.75 2.25 2.68 2.44 2.79 2.19 2.35 1.82 2.58 BIAS 0.99 0.73 1.84 1.55 1.64 1.51 0.28 1.75 2.15 0.6 0.68 1.44 2.04 1.06 -0.22 0.56 1.01 RMSE 2.44 2.43 3.35 2.86 3.34 2.71 3 2.97 3.38 2.82 3.25 3 3.35 2.72 3.14 2.48 3.18

T Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton

IOA 0.95 0.96 0.92 0.95 0.93 0.95 0.96 0.94 0.92 0.97 0.96 0.95 0.94 0.97 0.97 0.98 0.97 MAE 1.53 1.49 2.4 1.88 2.34 1.81 2.14 2 2.31 1.95 2.27 2.05 2.45 1.84 1.83 1.44 2.11 BIAS 0.65 0.44 1.46 1.16 1.23 1.11 -0.03 1.32 1.61 0.35 0.37 1.04 1.62 0.76 -0.51 0.26 0.66 RMSE 2.01 1.92 2.84 2.32 2.82 2.24 2.61 2.51 2.81 2.43 2.77 2.53 2.93 2.27 2.47 1.88 2.58


WS Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.8 0.76 0.57 0.77 0.7 0.62 0.71 0.79 0.72 0.78 0.8 0.75 0.73 0.79 0.61 0.85 0.76 MAE 1.55 1.44 2.28 1.7 1.64 2.49 1.54 1.43 1.32 1.58 1.53 1.67 1.7 1.36 2.33 1.21 1.59 BIAS -0.2 -0.22 -2.01 -0.43 -0.03 -1.87 -0.25 0.61 0.26 0.39 0.08 0.54 0.82 -0.4 -2.11 -0.55 -1.1 RMSE 2.01 1.95 2.77 2.26 2.21 2.99 1.94 1.85 1.71 2.03 2.05 2.17 2.16 1.8 2.8 1.57 1.99


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Table 8 Performance benchmarks by site 3 days ahead HLI Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.96 0.95 0.96 0.97 0.96 0.96 0.96 0.96 0.96 0.97 0.97 0.96 0.97 0.97 0.96 0.97 0.96 MAE 3.25 3.49 3.55 2.81 3.19 3.28 2.97 2.39 3.3 2.79 2.86 3.19 2.93 2.73 2.74 2.08 2.73 BIAS 0.49 1.44 2.5 0.75 1.35 2.09 1.22 0.75 -0.16 -0.23 0.32 0.3 -0.65 0.93 1.19 -0.68 1.02 RMSE 4.74 5.16 4.96 4.16 4.72 4.71 4.54 3.73 4.59 4.43 4.42 4.81 4.37 4.2 4.46 3.4 4.37

BGT Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.98 0.97 0.96 0.97 0.96 0.97 0.97 0.97 0.96 0.97 0.97 0.97 0.97 0.98 0.97 0.98 0.97 MAE 1.87 2.01 2.87 2.38 2.94 2.29 2.61 2.51 2.77 2.47 2.89 2.64 2.88 2.39 2.51 2.07 2.78 BIAS 0.93 0.85 1.81 1.59 1.63 1.58 0.22 1.85 2.07 0.62 0.66 1.54 2.14 1.11 -0.16 0.65 1.09 RMSE 2.46 2.66 3.44 2.99 3.59 2.86 3.2 3.18 3.41 3.19 3.57 3.23 3.48 3.04 3.34 2.75 3.49

T Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.95 0.95 0.92 0.94 0.92 0.94 0.95 0.93 0.92 0.96 0.95 0.93 0.93 0.96 0.97 0.97 0.96 MAE 1.58 1.61 2.49 2 2.5 1.96 2.29 2.19 2.37 2.17 2.49 2.27 2.55 2.04 2.02 1.65 2.3 BIAS 0.59 0.52 1.47 1.21 1.24 1.18 -0.1 1.42 1.56 0.36 0.36 1.15 1.71 0.81 -0.47 0.3 0.73 RMSE 2.06 2.11 2.99 2.47 3.05 2.41 2.82 2.74 2.9 2.78 3.07 2.78 3.05 2.59 2.7 2.13 2.88


WS Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.8 0.74 0.54 0.75 0.68 0.6 0.68 0.78 0.73 0.75 0.77 0.71 0.73 0.77 0.59 0.83 0.71 MAE 1.59 1.54 2.39 1.83 1.71 2.58 1.58 1.47 1.33 1.65 1.64 1.77 1.7 1.43 2.38 1.27 1.67 BIAS -0.24 -0.29 -2.1 -0.53 -0.03 -1.95 -0.27 0.67 0.22 0.45 0.18 0.61 0.85 -0.29 -2.05 -0.47 -0.98 RMSE 2.04 2.06 2.96 2.4 2.27 3.09 1.98 1.86 1.7 2.11 2.2 2.3 2.15 1.89 2.84 1.64 2.11


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Table 9 Performance benchmarks by site 4 days ahead HLI Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.96 0.95 0.95 0.97 0.95 0.96 0.96 0.96 0.96 0.97 0.97 0.96 0.97 0.97 0.95 0.97 0.96 MAE 3.29 3.55 3.77 2.84 3.52 3.48 3.08 2.49 3.36 2.9 2.97 3.41 2.87 2.83 2.97 2.23 2.89 BIAS 0.75 1.56 2.73 0.89 1.48 2.34 0.92 0.87 -0.24 -0.44 0.11 0.3 -0.49 0.74 1.32 -0.65 1.05 RMSE 4.75 5.15 5.28 4.16 5.18 5 4.67 3.88 4.71 4.5 4.42 5.18 4.26 4.31 4.73 3.58 4.57 BGT Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.98 0.97 0.96 0.97 0.95 0.97 0.97 0.96 0.96 0.97 0.96 0.96 0.96 0.97 0.97 0.98 0.97 MAE 1.99 2 2.97 2.45 3.08 2.41 2.66 2.63 2.88 2.63 3.03 2.82 3.04 2.55 2.73 2.2 2.92 BIAS 1.1 0.87 1.9 1.68 1.7 1.66 0.04 1.72 1.94 0.45 0.62 1.42 2.05 1.01 -0.15 0.52 1 RMSE 2.6 2.64 3.58 3.07 3.74 3.05 3.33 3.31 3.57 3.41 3.81 3.5 3.66 3.24 3.61 2.91 3.68 T Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.95 0.95 0.91 0.93 0.91 0.93 0.95 0.92 0.91 0.95 0.95 0.92 0.92 0.96 0.96 0.97 0.96 MAE 1.66 1.6 2.54 2.07 2.63 2.05 2.38 2.33 2.51 2.3 2.63 2.43 2.71 2.19 2.26 1.8 2.48 BIAS 0.74 0.53 1.51 1.29 1.31 1.24 -0.27 1.3 1.46 0.2 0.31 1.05 1.65 0.71 -0.47 0.19 0.63 RMSE 2.16 2.09 3.07 2.55 3.2 2.56 3 2.92 3.09 3.03 3.32 3.05 3.26 2.8 3.01 2.33 3.11 RH Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.9 0.9 0.84 0.89 0.84 0.86 0.84 0.88 0.82 0.86 0.88 0.83 0.84 0.89 0.88 0.9 0.92 MAE 9.47 9.6 13.89 11.19 12.77 11.92 12.11 11.76 14.8 10.79 9.97 12.32 13.98 9.73 11.94 11.97 9.36 BIAS -2.85 2.51 -3.73 -5.12 -1.56 -4.21 6.18 -1.52 -8.39 -0.18 -0.38 -3.64 -8.81 -0.44 5.37 -7.09 -2.45 RMSE 12.71 14.05 17.76 14.46 17.45 15.61 16.32 16.68 18.26 15.28 14.1 17.23 17.01 13.68 16.87 14.88 12.91 WS Amberley Emerald Miles Oakey Roma Warwick Albury Armidale Cessnock Griffith Hay Moree Tamworth Yanco Clare Katanning Charleton IOA 0.78 0.71 0.52 0.73 0.65 0.57 0.66 0.76 0.69 0.73 0.73 0.67 0.68 0.74 0.57 0.8 0.71 MAE 1.67 1.72 2.46 1.96 1.81 2.77 1.63 1.53 1.39 1.7 1.77 1.89 1.81 1.47 2.36 1.39 1.64 BIAS -0.31 -0.45 -2.13 -0.66 -0.06 -2.14 -0.25 0.47 0.26 0.5 0.27 0.55 0.72 -0.24 -2 -0.55 -0.88 RMSE 2.14 2.31 3.05 2.54 2.41 3.32 2.05 1.96 1.78 2.18 2.36 2.44 2.32 1.93 2.89 1.76 2.08


Page 25 of 114

Table 10 1 and 2 day forecast AHLU contingency tables for all locations for the forecast season (forecast is on the horizontal and observed on the vertical)

1 Day

2 Day

AHLU 80

AHLU 80

Extreme 0.09% 0.09% 0.49% 1.88%

Extreme 0.18% 0.18% 0.74% 1.45%

High 0.40% 1.11% 1.45% 0.07%

High 0.76% 1.17% 0.98% 0.13%

Medium 2.47% 2.77% 0.45% 0.01%

Medium 3.10% 2.09% 0.48% 0.03%

Low 86.62% 1.87% 0.20% 0.02%

Low 86.69% 1.84% 0.18% 0.02%

Low Medium High Extreme


AHLU 83

AHLU 83

Extreme 0.03% 0.00% 0.07% 0.24%

Extreme 0.06% 0.03% 0.05% 0.20%

High 0.20% 0.56% 0.50% 0.04%

High 0.32% 0.50% 0.46% 0.02%

Medium 1.65% 1.36% 0.17% 0.01%

Medium 1.92% 1.04% 0.20% 0.02%

Low 94.16% 0.92% 0.05% 0.04%

Low 94.16% 0.94% 0.06% 0.01%



AHLU 86

AHLU 86

Extreme 0.02% 0.00% 0.01% 0.01%

Extreme 0.05% 0.00% 0.00% 0.00%

High 0.04% 0.08% 0.12% 0.00%

High 0.08% 0.05% 0.08% 0.03%

Medium 0.87% 0.47% 0.03% 0.00%

Medium 0.93% 0.39% 0.05% 0.00%

Low 98.01% 0.34% 0.00% 0.00%

Low 97.97% 0.36% 0.02% 0.00%



AHLU 89

AHLU 89

Extreme 0.02% 0.00% 0.00% 0.00%

Extreme 0.02% 0.00% 0.00% 0.00%

High 0.01% 0.03% 0.01% 0.00%

High 0.04% 0.00% 0.02% 0.00%

Medium 0.16% 0.05% 0.00% 0.00%

Medium 0.13% 0.07% 0.00% 0.00%

Low 99.63% 0.09% 0.00% 0.00%

Low 99.59% 0.12% 0.00% 0.00%



AHLU 92

AHLU 92

Extreme 0.01% 0.00% 0.00% 0.00%

Extreme 0.01% 0.00% 0.00% 0.00%

High 0.02% 0.00% 0.00% 0.00%

High 0.02% 0.00% 0.00% 0.00%

Medium 0.03% 0.02% 0.00% 0.00%

Medium 0.03% 0.02% 0.00% 0.00%

Low 99.92% 0.00% 0.00% 0.00%

Low 99.91% 0.01% 0.00% 0.00%



AHLU 95

AHLU 95

Extreme 0.00% 0.00% 0.00% 0.00%

Extreme 0.00% 0.00% 0.00% 0.00%

High 0.02% 0.00% 0.00% 0.00%

High 0.02% 0.00% 0.00% 0.00%

Medium 0.01% 0.00% 0.00% 0.00%

Medium 0.02% 0.00% 0.00% 0.00%

Low 99.97% 0.00% 0.00% 0.00%

Low 99.97% 0.00% 0.00% 0.00%




Page 26 of 114

Table 11 3 and 4 day forecast AHLU contingency tables for all locations for the forecast season

3 Day

4 Day

AHLU 80

AHLU_80

Extreme 0.33% 0.25% 0.69% 1.27%

Extreme 0.34% 0.33% 0.74% 1.18%

High 0.82% 1.10% 0.97% 0.14%

High 0.89% 1.13% 0.89% 0.11%

Medium 3.33% 1.84% 0.51% 0.03%

Medium 3.50% 1.85% 0.39% 0.02%

Low 86.84% 1.66% 0.19% 0.02%

Low 86.81% 1.53% 0.29% 0.01%



AHLU 83

AHLU_83

Extreme 0.09% 0.03% 0.07% 0.15%

Extreme 0.07% 0.01% 0.07% 0.20%

High 0.38% 0.53% 0.35% 0.03%

High 0.40% 0.51% 0.33% 0.05%

Medium 1.94% 1.07% 0.17% 0.01%

Medium 2.14% 0.87% 0.19% 0.00%

Low 94.21% 0.87% 0.07% 0.02%

Low 94.27% 0.80% 0.08% 0.01%



AHLU 86

AHLU_86

Extreme 0.05% 0.00% 0.00% 0.00%

Extreme 0.05% 0.00% 0.00% 0.00%

High 0.07% 0.06% 0.09% 0.01%

High 0.05% 0.08% 0.10% 0.01%

Medium 0.92% 0.39% 0.06% 0.00%

Medium 0.92% 0.38% 0.05% 0.01%

Low 97.98% 0.35% 0.02% 0.00%

Low 98.03% 0.31% 0.02% 0.00%



AHLU 89

AHLU_89

Extreme 0.02% 0.00% 0.00% 0.00%

Extreme 0.02% 0.00% 0.00% 0.00%

High 0.04% 0.00% 0.02% 0.00%

High 0.04% 0.02% 0.00% 0.00%

Medium 0.14% 0.05% 0.01% 0.00%

Medium 0.16% 0.05% 0.00% 0.00%

Low 99.60% 0.12% 0.01% 0.00%

Low 99.62% 0.09% 0.01% 0.00%



AHLU 92

AHLU_92

Extreme 0.01% 0.00% 0.00% 0.00%

Extreme 0.01% 0.00% 0.00% 0.00%

High 0.02% 0.00% 0.00% 0.00%

High 0.02% 0.00% 0.00% 0.00%

Medium 0.03% 0.02% 0.00% 0.00%

Medium 0.05% 0.00% 0.00% 0.00%

Low 99.89% 0.04% 0.00% 0.00%

Low 99.92% 0.00% 0.00% 0.00%



AHLU 95

AHLU_95

Extreme 0.00% 0.00% 0.00% 0.00%

Extreme 0.00% 0.00% 0.00% 0.00%

High 0.02% 0.00% 0.00% 0.00%

High 0.02% 0.00% 0.00% 0.00%

Medium 0.02% 0.00% 0.00% 0.00%

Medium 0.02% 0.00% 0.00% 0.00%

Low 99.97% 0.00% 0.00% 0.00%

Low 99.97% 0.00% 0.00% 0.00%




Page 27 of 114

5.5 Subscriber survey

At the end of this season, a survey was sent out to all CHLT subscribers, asking them to rate the service and provide comment on where the service could be improved. The survey was also made available on the website, for visitors to the CHLT website service who are not registered. Detailed results are shown in Appendix B. 5.5.1 User Profiles

The survey received a total of 40 responses, 38 from registered users and two non registered users that rely on the publicly available sites. Sixteen out of the 38 registered users are site administrators, while the rest are subscribers (one respondent skipped the question). Most (36) of the respondents are feedlot operators. One veterinarian, nutritionist, policy officer, and WHS/QA supervisor also responded to the survey. The non registered CHLT users who access the CHLT service solely though the public pages visit the pages infrequently (every now and then or only when there’s an event). Both users view forecast for locations greater than 25 km away from their site. Both users do not have future plans of registering for the service. One of the subscribers does not have a registered feedlot, while the other subscriber did not register as the registration process is assessed to be too long and too confusing. 5.5.2 Usage

Half of the respondents (20) used the CHLT service at least once daily. Fifteen respondents also use the CHLT service periodically, but less frequently. The remaining five respondents only check the service when there is a potential issue. The 38 registered subscribers rated the frequency of visits to the individual pages within the CHLT website.

• The following pages are visited most frequently on a daily or weekly basis: o Heat event and weather warnings o Site summary o Detailed site hourly forecast

• Approximately a third of the respondents visit the following sites on a less frequent basis:

o Australia-wide HLI and MSLP forecast o Tools (RAP and HLI calculator) o Major town forecasts and observations o Help files

5.5.3 Ease of Use

Clarity of information

The 38 registered subscribers rated the clarity of the pages within the CHLT website. Most of the subscribers considered the pages to be clear. One respondent assessed all the pages


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as unclear, however this subscriber does not use the CHLT service on a regular basis. The following pages were evaluated:

• Heat event and weather warnings • Site summary • Detailed site hourly forecast • Australia-wide HLI and MSLP forecast • Tools (RAP and HLI calculator) • Major town forecasts and observations • Help files

The respondents who evaluated the publicly-available pages from the website considered all pages to be clear. Account management

Twenty-two of the registered subscribers utilise the online account management option. Most of the respondents considered online account management easy to use. Two subscribers considered updating login details and alert settings as somewhat difficult to use. One subscriber also considered updating contact details somewhat difficult to use. Thirteen of the users who use the account management options are site administrators. Most of the site administrators consider the web-related functionalities relating to management of subscribers easy to use. One site administrator considered that adding subscribers and editing their details as somewhat difficult. All site administrators assessed that updating alert settings are easy. Thirty-one subscribers responded to queries regarding the clarity of information used in setting alerts. Most of the registered users consider information regarding the following easy:

• understanding HLI • understanding and selecting the right AHLU level • understanding and selecting the right risk level

There are a few subscribers who consider the same information somewhat difficult. One subscriber also considers selecting the right AHLU level very difficult. 5.5.4 Alerts

Thirty-one subscribers responded to queries regarding alerts. More than half of the respondents use AHLU from the RAP calculator and HLI to set their alert settings. A small number (2) rely on default AHLU levels (which there isn't one) or AHLU from other sources. One respondent relies on the site administrator to set the alerts. None of the respondents used the help file. Most respondents (26) have not changed their alert settings since the start of the season. Four subscribers change their alert settings every now and then, while one subscriber changes it a few times a month. This is concerning as the use the service most effectivley a site manager should change the AHLU level depending on type of cattle, days on feed and other feedlot management options. According to the survey, the alert settings would be changed due to the following reasons:

• Change in cattle stock characteristics (16)


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• Change in management practice (11) • Change in mitigation measures (8) • Getting too many alerts (3) • Getting too few alerts (3)

Most respondents (22) assess that they are receiving the right amount of alerts. Six assess that they are receiving too few alert, and 3 think that they receive too many. Most respondents (27) assess that they are receiving the alerts at the best time to make a decision. However, four of the respondents do not agree. Two respondents assess that they did not receive alerts, while one respondent think that the alerts are being sent out too early in the morning. 5.5.5 Performance

Thirty-five of the respondents responded to survey questions regarding the accuracy of the forecast. With the exception of 5 subscribers, the remaining 30 respondents assess the forecast to be accurate. Comments relating to forecast accuracy address the conservatism of the forecast. Most of the respondents (24) consider that the forecast predicted all the major heat events at their sites. A few (9) assess that the forecast predicted some of the heat events that occurred. One subscriber did not experience a heat event at his site. One subscriber assessed that there were between 2-5 events at his site, and that the event was not forecast. Technically, there are only ten sites uploading AWS data to be used in the forecast. However, there are 18 subscribers who confirmed connection to the HLDN. This number may include subscribers who are still in the process of uploading their data. Most of the subscribers assess that connecting to the HLDN improved their forecast a little (8) or a lot (5). Some subscribers have intentions of connecting to HLDN, but are either working on issue or require more information. A small number are not connected due to not having an AWS or being a veterinarian/nutritionist. Two subscribers are not connected due to difficulty in retrieving data. Only 50% of subscribers not connected to HLDN are aware of the funding assistance from MLA.


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6. Discussion

The 2013/14 summer forecasting season saw a significant improvement to the accuracy and delivery of heat load warnings than any year in the history of the service. Several improvements to the underlying terrain data and the initialisation model have been shown to improve the accuracy of the forecast service. Most notably since the introduction of the K-WRF system year on year performance has improved compared to pre K-WRF years. With the introduction of the forecast cycling step and background error statistics Katestone is confident that performance will continue to improve. The introduction of written Heat Load Advisory service identified potential heat events up to 7 days ahead and the automated alert thresholds were triggered 3 to 4 days ahead of most events. Rapid changes in the HLI were identified by the K-WRF system and confirmed by the Katestone team prior to posting an Advisory on the CHLT site and sending it out via email. These events are identified by experience and require a human forecaster to monitor the situation as it evolves. Further work is required to identify a threshold change in HLI to automate this type of alert. The CHLT service has successfully integrated onsite AWS data into the forecast AHLU levels for 14 feedlots with more in progress. This has been perceived by the users as having improved the forecast AHLU for their site. Sites can now manage their own users, adding and removing subscribers to their site online. This is particularly useful for vets and nutritionists that interact with several feedlots. Uptake of this feature has been limited however. This is thought to be due a lack of knowledge of the capability itself and its usage. 6.1 Future improvements

The proposed upgrade to the service for the 2014/15 season include:

• Extension of high resolution forecast to seven days (previously only four) • Ability of registered users to set their own alert time • Extension to include the Woolworths Quality Assurance (WQA) scheme for CHLT

registration purposes

The proposed service upgrades are based on feedback from registered users during the end of season survey, working upgrades required and improvements based on identified constraints. A copy of the survey results will be included with the end of season report. This season Katestone has been testing a cycling algorithm and background error statistic to initialise the land surface model from the previous forecast and will include data assimilation from the WMO surface and upper air observations in locations that have extremely localised features. The data assimilation and bias correction steps proposed for last season are currently not fully implemented. Initial testing indicated that unknown and at times undesirable impacts on different aspects of the model were experienced. Therefore it was decided that changing the process during the core operational period would be detrimental to the service. The testing therefore has been progressing offline until after March 31. The period between April 1 and November 1 will see the implementation of the cycling step in an operational context where it can be debugged with minimal impact of the core operational period.


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7. Conclusion

A heat load forecast service was provided for the 2013/14 summer period to over 241 locations. The service delivered automated alerts and written advisories for major heat load events. The service will continue throughout the year providing email only alerts and daily forecasts to all sites. The numerical weather prediction system, K-WRF, performed well in simulating the meteorological conditions that drive heat load events and in predicting the HLI and AHLU risk levels. The automated alert system needs to be expanded to encompass a broader range of adverse conditions, including rapid onset situations following intense rainfall. Greater emphasis also needs to be placed on education concerning the system and its operation. Integrating onsite AWS data into the service is currently underway as is improving the algorithms that underlay the HLI and AHLU. Further work is required to update the RAP database.


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8. Bibliography

http://www.metoffice.gov.uk/research/modelling-systems/unified-model/weather-forecasting

http://www.bom.gov.au/australia/charts/about/about_access.shtml

http://www.ecmwf.int/products/forecasts/guide/The_ECMWF_global_atmospheric_model.html

http://www.emc.ncep.noaa.gov/index.php?branch=GFS

EA Systems (2004), “FLOT. 327 – Development of a Heat Load Risk Assessment Process for the Australian feedlot industry”. Gaughan J, Goopy J and Spark J (2002), “Excessive Heat Load Index for Feedlot Cattle”, University of Queensland. Liu, Y. and co-authors, 2008: The operational mesogammascale analysis and forecast system of the U.S. Army Test and Evaluation Command. Part I: Overview of the modeling system, the forecast products, and how the products are used. J. of Appl. Meteor. and Clim., 47, 1077-1092. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M.G. Duda, X-Y. Huang, W. Wang and J. G. Powers, 2005: A description of the Advanced Research WRF Version 3. NCAR Technical Note, NCAR/TN-475+(STR), National Center for Atmospheric Research, Boulder, CO, 125 pp.

http://www.metoffice.gov.uk/research/modelling-systems/unified-model/weather-forecasting

http://www.bom.gov.au/australia/charts/about/about_access.shtml



http://www.emc.ncep.noaa.gov/index.php?branch=GFS


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Appendix A AHLU Performance

A1. Day 1

Oakey

AHLU_80 Extreme 0.00% 0.00% 0.00% 0.00% High 0.01% 0.70% 0.93% 0.00% Medium 2.63% 2.35% 0.38% 0.00% Low 91.77% 1.23% 0.00% 0.00% Low Medium High Extreme AHLU_83 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.26% 0.05% 0.00% Medium 0.74% 0.60% 0.08% 0.00% Low 97.85% 0.43% 0.00% 0.00% Low Medium High Extreme AHLU_86

Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00%

Medium 0.22% 0.16% 0.00% 0.00% Low 99.48% 0.14% 0.00% 0.00%

Low Medium High Extreme AHLU_89 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00% Medium 0.00% 0.00% 0.00% 0.00% Low 100.00% 0.00% 0.00% 0.00% Low Medium High Extreme AHLU_92 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00% Medium 0.00% 0.00% 0.00% 0.00% Low 100.00% 0.00% 0.00% 0.00% Low Medium High Extreme AHLU_95 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00% Medium 0.00% 0.00% 0.00% 0.00% Low 100.00% 0.00% 0.00% 0.00% Low Medium High Extreme


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Warwick

AHLU_80 Extreme 0.00% 0.10% 0.30% 0.00% High 0.53% 1.35% 1.00% 0.00% Medium 3.63% 2.21% 0.29% 0.00% Low 88.71% 1.87% 0.01% 0.00% Low Medium High Extreme AHLU_83 Extreme 0.00% 0.00% 0.00% 0.00% High 0.26% 0.42% 0.00% 0.00% Medium 1.94% 1.12% 0.00% 0.00% Low 95.86% 0.34% 0.05% 0.00% Low Medium High Extreme AHLU_86 Extreme 0.00% 0.00% 0.00% 0.00% High 0.10% 0.00% 0.00% 0.00% Medium 0.68% 0.00% 0.00% 0.00% Low 99.04% 0.18% 0.00% 0.00% Low Medium High Extreme AHLU_89 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00% Medium 0.23% 0.00% 0.00% 0.00% Low 99.77% 0.00% 0.00% 0.00% Low Medium High Extreme AHLU_92 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00% Medium 0.00% 0.00% 0.00% 0.00% Low 100.00% 0.00% 0.00% 0.00% Low Medium High Extreme AHLU_95 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00% Medium 0.00% 0.00% 0.00% 0.00% Low 100.00% 0.00% 0.00% 0.00% Low Medium High Extreme


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Tamworth



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Roma



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Amberley



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Yanco



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Miles



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Katanning



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Hay



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Griffith



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Emerald



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Clare



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Charlton



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Cessnock



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Armidale



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Albury



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Moree



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A2. Day 2

Oakey



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Warwick

AHLU_80 Extreme 0.00% 0.00% 0.04% 0.36% High 0.67% 1.46% 0.71% 0.04% Medium 4.19% 1.82% 0.12% 0.00% Low 88.69% 1.82% 0.08% 0.00% Low Medium High Extreme AHLU_83 Extreme 0.00% C 0.00% 0.00% High 0.04% 0.08% 0.56% 0.00% Medium 2.42% 0.64% 0.00% 0.00% Low 95.74% 0.52% 0.00% 0.00% Low Medium High Extreme AHLU_86 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.10% 0.00% Medium 0.16% 0.46% 0.05% 0.00% Low 99.15% 0.07% 0.00% 0.00% Low Medium High Extreme AHLU_89 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00% Medium 0.07% 0.16% 0.00% 0.00% Low 99.74% 0.03% 0.00% 0.00% Low Medium High Extreme AHLU_92 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00% Medium 0.00% 0.00% 0.00% 0.00% Low 100.00% 0.00% 0.00% 0.00% Low Medium High Extreme AHLU_95 Extreme 0.00% 0.00% 0.00% 0.00% High 0.00% 0.00% 0.00% 0.00% Medium 0.00% 0.00% 0.00% 0.00% Low 100.00% 0.00% 0.00% 0.00% Low Medium High Extreme


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Tamworth



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Roma



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Amberley



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Yanco



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Miles



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Katanning



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Hay



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Griffith



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Emerald



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Clare



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Charlton



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Cessnock



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Armidale



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Albury



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Moree



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A3. Day 3

Oakey



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Warwick



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Tamworth



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Roma



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Amberley



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Yanco



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Miles



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Katanning



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Hay



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Griffith



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Emerald



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Clare



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Charlton



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Cessnock



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Armidale



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Albury



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Moree



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A4. Day 4

Oakey



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Warwick



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Tamworth



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Roma



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Amberley



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Yanco



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Miles



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Katanning



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Hay



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Griffith



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Emerald



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Clare



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Charlton



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Cessnock



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Armidale



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Albury



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Moree



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Appendix B Survey Results

Subscribers 38 Subscriber Status Administrator 16 Subscriber 22 Skipped question 1 Account Management Uses account management options 22 Does not use account management options 15 Skipped question 1 Registered for Alerts SMS only 1 Email only 11 SMS and Email 19 Not registered 4 skipped question 3 Non-subscribers 2 B1. General Information

Frequency of visit to the Cattle Heat Load Toolbox (CHLT) website

Total Responses 40


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Role in operations

Total Responses 40


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B2. Registered Subscriber

B1. User Administration

Ease of use (administrator)

Total Responses 13

Ease of use (subscriber)

Total Responses 22

Frequency of visit on CHLT pages


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Total Responses 38

Clarity of CHLT Subscriber pages

Total Responses 38


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Information reviewed

Total Responses 38


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B2. Forecast Accuracy

Accuracy of the forecast

Other comments:

tended to be under weather station rel time data Most of the time the temperatures / predictions were conservative compared to our own weather station Temperature was usually a few degrees under other weather sites affecting HLI forcast understimates RH and wind speed

Total Responses 35


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Heat load events

Comments:

no major heat stress events this season Did predict them but data still conservative

Total Responses 35


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B3. Heat Load Data Network (HLDN)

Connected 18 Forecast performance Improved my forecast a lot 5 Improved my forecast a little 8 Did not improve my forecast

• Wasn't sure if we were connected properly or not • only connected at end of season - still waiting on link from

Corporate IT

3

Made forecast worse 0 Cannot tell 2 Not connected 16 Reasons for not connecting: Still working on issues but will be connecting very soon 3 Have plans of connecting, but need more information 6 Don't have an AWS

• Don't operate a feedlot / veterinarian • The AWS is not specific to the Feedlot

5

Difficulty in retrieving data 2 Awareness of availability of funding Yes 8 No 8


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B4. Alerts

Frequency of updating alert settings

Other comments:

depends if it’s summer or winter Total Responses 31


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Reasons for updating alerts

Total Responses 30

Frequency of alerts

Total Responses 31


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Timing of alerts (Alerts received at the best time to support decisions)

Other comments:

not receiving them this season AI never got any SMS alerts to my mobile Much too early in the morning

Total Responses 31


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Information used to set alerts

Total Responses 31

Clarity / ease of understanding

Other comments:

The key is to have the risk level relevant. A couple of places on site that risk can be managed, e.g. RAP calcuter, Risk level & AHLU level. Perhaps there is value in providing additional information on how these factors interlate and how feedlot can apply these factors to best identify risk for site.

Total Responses 31


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B3. Unregistered respondents

Summary

Location Total Responses 2 Location (QLD) Constance St, Miles (Distance: 25-50 km) (QLD) Dalby Airport (Distance: > 50km) Reason for viewing to get HLI info Interest Frequency of Visits Yesterday's Observations Only when there's an

event Hourly Forecast Detail Heat event and weather warnings Every now and then /

Only when there's an event

Australia-wide MSLP forecast Only when there's an event Australia-wide HLI forecast

Tools > RAP calculator Every now and then / Only when there's an

even Tools > HLI calculator

Help files Every now and then Clarity of Pages Yesterday's Observations

Clear

Hourly Forecast Detail Heat event and weather warnings Australia-wide MSLP forecast Australia-wide HLI forecast Tools > RAP calculator Tools > HLI calculator Help files Information Reviewed AHLU 80 2 AHLU 83 2 AHLU 86 2 AHLU 89 1 AHLU 92 1 AHLU 95 2 HLI 2 BGT 2 Temperature 2 Relative Humidity 2 Wind Direction 1 Wind Speed 1 Rain 2 Reason for not registering I do not have a registered feedlot. 1 I don't use it enough to register. 0 The registration process is too confusing. 1 The registration process is too long. 1 (Other) Just looking for information 1


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Future plans for registration Yes 0 No 1 I will only register if I am forced to 1

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