PROPOSAL Consultancy to establish an integrated early warning system for REMA Technical Proposal: approach, methodology and work plan Proposal being submitted to: RWANDA ENVIRONMENT MANAGEMENT AUTHORITY: TENDER REF: No 022/REMA/2011- 2012 Principal Investigator Thomas M. Hopson, Ph.D. National Center for Atmospheric Research P.O. Box 3000 Boulder, CO 80307 Ph: +1 303-497-2706 Fax: +1 303-497-8401 Email: [email protected]Co-Principal Investigators Brant Foote, Arnaud Dumont, and David Gochis National Center for Atmospheric Research P.O. Box 3000 Boulder, CO 80307 Start Date: 01 October 2012 End Date: 30 March 2014 Duration: 1.5 years 1
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PROPOSAL
Consultancy to establish an integrated early warning system for REMA
Technical Proposal: approach, methodology and work plan
Proposal being submitted to:
RWANDA ENVIRONMENT MANAGEMENT AUTHORITY: TENDER REF: No 022/REMA/2011-2012
Principal Investigator
Thomas M. Hopson, Ph.D.National Center for Atmospheric Research
Rwanda has experienced flash floods impacting urban areas that are difficult to measure and anticipate. As a result, the country is in the process of improving both its measurement capability as well as its flash flood forecasting capabilities through installation of advanced weather and forecast hardware and software, and through training operational personnel. In addition, the country’s meteorological office is establishing closer collaborations with disaster management institutions and collaborations with local communities to configure early warning systems and strengthen dissemination capacity. To help meet these needs, the Research Applications Laboratory (RAL) of the National Center for Atmospheric Research (NCAR) offers its science and technology capability to enhance Rwanda’s capacity to meet its forecasting requirements.
Given the above context of Rwanda’s vulnerability to severe flooding events and current lack of established infrastructure, NCAR/RAL proposes a phased response to enable Rwanda to establish a foundation to build longer-term infrastructure and technical staff capacity. This Phase-1 proposal entails (1) an assessment of current infrastructure, capacity, user needs, and training; (2) acquiring, assessing, and utilizing publicly-available data resources; (3) implementing a data and early warning dissemination and visualization infrastructure that will integrate publicly available products and will have the flexibility to incorporate advanced future data sets (e.g., radar, rain gauges, streamflow observations, advanced NWP forecasting); and (4) consulting with and training key Rwanda personnel to anticipate and utilize future data resources that would have relevancy to precipitation and flooding Early Warning Systems (EWS). In this context, we have broken down the tasking of this proposal into four areas: early assessment, development of publicly-available data utilization for EWS, decision support (DSS), and capacity enhancement through consultation/training. We discuss each of these pieces individually below, providing a time-line and tasking of deliverables at the end of this document.
2.1 Assessment of Capabilities and Needs____________________________________________________________6Task 1: Prepare for Site Visits by Collecting and Reviewing Background Information_______________________7Task 2: Conduct Site Visit__________________________________________________________________________________________7Task 3: Analyze Results of Site Visit and Prepare Report_______________________________________________________8
2.2 Utilization of publicly available data sources___________________________________________________92.2.1 Satellite-based precipitation estimation__________________________________________________________________102.2.2 Thorpex-Tigge Medium-range Weather prediction______________________________________________________10
2.3 Information Management and Decision Support Systems (DSS) for Weather and Flood Forecasting______________________________________________________________________________________________10
2.3.1 Information Management__________________________________________________________________________________102.3.2 Standardized Formats and Interfaces_____________________________________________________________________112.3.3 Data Repository and Replication__________________________________________________________________________112.3.4 Decision Support____________________________________________________________________________________________122.3.5 Data Services________________________________________________________________________________________________13
3.2 Utilization of publicly-available large-scale datasets_________________________________________143.2.1 Tasks_________________________________________________________________________________________________________143.2.2 Budget________________________________________________________________________________________________________153.2.3 Timeline_____________________________________________________________________________________________________15
3.3 Information Management and Decision Support Systems (DSS) for Weather and Flood Forecasting______________________________________________________________________________________________15
1.1 A roadmap to developing a state-of-the-art Early Warning System
Acquiring a truly effective, state-of-the-art environmental Early Warning (EWS) System and decision support tool requires development across many different levels (or types) of infrastructure. Such a system requires in-situ (e.g., rain gauge networks) and remotely-sensed (e.g., radar) data sources, algorithmic developments, data transfer and visualization to meet specific user needs, training and capacity building, and awareness. In Figure 1, we diagrammatically highlight these required levels of development.
Fig. 1: Early Warning System linkages for weather-related hazards
1.2 Scope of the current proposal
Developing the EWS described in Figure 1 is an extremely significant undertaking which has three underlying requirements:
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Requirement 1 : Good understanding of the physical environment, observational and modelling infrastructure, and human resources as they currently exist, as well as understanding what is needed by decision makers at the local and national levels.
Requirement 2 : Local-scale information from rain gauge networks and satellites coupled with meteorological and hydrological numerical prediction systems tuned to the local region (Levels 1 and 2).
Requirement 3 : An integration and product generation and dissemination system designed within the framework of Requirements 1 and 2 that addresses the user needs and societal vulnerabilities (Level 3).
We see clear recognition in the terms of reference of the need for an assessment of needs and capabilities within Rwanda (addressing requirement #1 above); and we note that REMA is developing networks of rain gauges and installing radars under separate tenders (addressing part of requirement #2 above). However, while solid plans are in place, the above three requirements do not currently exist. In our opinion it is not possible to create a state-of-the-art early warning system (e.g., provide accurate quantitative flood forecasts) without local scale information.
In terms of scope, we propose a multi-phased program leading to establishment of an EWS as described in the terms of reference. In this proposal we describe work that will cover Phase 1 of the program. This phase will address many of items identified in the terms of reference, including:
1. Assessment activities as identified in the terms of reference, and including: assessing current forecasting capabilities for the region; reviewing available model and observation data sets and their performance over the region; identifying and understanding user needs; identifying ways to accelerate improvements in the prediction of high-impact events, understanding what is needed to improve current forecasts using satellite data and ensemble model forecasts; investigating modeling needs; examining current communications infrastructure; and evaluating needs for in-country training.
2. The development of a preliminary and basic decision support tool that will initially ingest currently-available data, including that from satellites and global-scale numerical weather prediction models, and any available ground-based observational systems. This tool will be custom-built for REMA and will serve as the engine for the fully-fledged EWS developed in future phases of the project.
A future Phase 2 (or perhaps multiple phases) of the project – not described in this proposal – is needed to focus on integrating new sources of data as they become available (e.g., data from rain-gauge networks and radar that are currently under development, and from advanced numerical prediction systems) so as to provide new advanced forecast capabilities to the nation. Infrastructure acquisitions currently being pursued by the Government of Rwanda will drive this progress. The addition of a radar, for example, will allow us to bring NCAR’s world-class nowcasting expertise to the EWS. The availability of cluster computing will allow us to bring our extensive skill in atmospheric and hydrological numerical modeling to the EWS. Improved ability to assimilate observational and modeling data will allow us to create a cutting-edge decision support system, another capability for which NCAR is well-known.
It is acknowledged that we are not proposing to provide all of the deliverables described in the terms of reference. In good faith, we do not believe that is possible to promise these things before
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conducting a detailed assessment of existing infrastructure, detailed user needs, and human capacity, and before rain gauge networks are established and a meteorological radar is operational. We have extensive experience building end-to-end decision support systems and can attest that the required research, development, technology transfer and training effort requires multi-year institutional commitments and flexibility to evolving (and often unanticipated) data and computational resources to be responsibly carried out. However, the work proposed in a Phase 1 here by NCAR will put REMA firmly on the path to achieving its goals by conducting a thorough assessment, providing some immediate improvements in forecasting skills, and planning and laying the framework for the development and deployment of an advanced operational EWS.
2. “Phase 1” Activities
2.1 Assessment of Capabilities and Needs
An assessment of current forecast capabilities and the needs of the nation of Rwanda is the first, and most critical, task to be performed by NCAR. To do this, we propose to engage the REMA sponsor in a dialogue concerning the “art of the possible.” By this we mean an interactive process in which we exchange information to identify needs and challenges on the user’s side and the kinds of improvements science and technology can currently provide. The outcome from this task will be a realistic, honest appraisal of current capabilities and needs, prioritization of those needs, and identification of the requirements for a state-of-the-art early warning system.
This approach to assessing a new sponsor’s needs and baseline capabilities has been successfully used by NCAR in research and development projects for U.S. mission agencies such as the Federal Aviation Administration, NOAA, NASA, and Department of Defense, as well as for government agencies in Hong Kong, Taiwan, Europe, Latin America, the Middle East, and Australia.
NCAR has assembled a scientific and technical team with extensive, relevant experience in a variety of fields essential to the success of the assessment. This team will work closely with REMA to assess the current state of forecasting capabilities and needs in Rwanda. The evaluation effort will be a collaborative effort, combining the experience and expertise of NCAR team with the expert, in-country knowledge of REMA staff.
Core NCAR Team: Dr. Tom Hopson: Program Manager, hydrologist with ensemble modeling expertise. Dr. Brant Foote: Laboratory Director and senior atmospheric scientist with extensive
international experience in assessment of forecasting systems and field programs. Dr. David Gochis: Senior hydrometeorologist, with experience in design of rain gauge
networks, and experience in development of coupled atmosphere-hydrology prediction models.
Mr. Arnaud Dumont: Senior software engineer, lead developer of numerous complex decision support systems.
Mr. Tor Mohling: Senior systems administrator/information technology expert.
Additional Expert Resources:
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One of the strengths of the NCAR proposal is the large reservoir of talented staff members available to the REMA effort. As specific questions or issues arise, experts on the NCAR staff will be consulted to make use of their in-depth skills and insights. Some examples include:
Mr. Jim Wilson and Ms. Rita Roberts: Senior researchers and international leaders in radar meteorology and short-term forecasting (nowcasting).
Ms. Cathy Kessinger: Senior researcher and expert in satellite meteorology. Mr. John Exby: Senior systems administrator and expert in cluster computing. Dr. Paul Kucera: Senior researcher and expert in precipitation instrumentation.
The assessment of forecasting capabilities will be conducted in three phases: 1. NCAR will provide a questionnaire to REMA to obtain as much information as possible up-
front; the NCAR team will closely review this information to gain an understanding of the nation’s current capabilities and needs before traveling to Rwanda;
2. NCAR will send a team of scientific and technical experts to Rwanda to meet with officials in Kigali and staff in study regions, learn about the nation’s rivers, watersheds and topography, and get a hands-on look at existing infrastructure;
3. After the site visit, the NCAR team will follow-up with REMA to ask questions and exchange information, and will prepare a report documenting results of the assessment task.
To enhance the collaborative exchange of information and to begin the important training component of the program, NCAR team members would be pleased to present a number of short introductory lectures of relevance to REMA (with further materials on these topics provided during the training at the end of the consultancy period of performance). Specific topics could include:
Severe Rain and Flooding Early Warning Systems of the U.S. Thunderstorm Nowcasting Radar Meteorology Satellite Meteorology The Weather Research and Forecasting (WRF) Model Hydrological Modeling Optimal Decision Support Strategy
Task 1: Prepare for Site Visits by Collecting and Reviewing Background Information The initial step in the assessment of existing forecasting capabilities and needs will be a review of relevant background materials supplied by REMA. These materials will be reviewed in advance of the main site visit by the NCAR technical team. Examples of information useful to this purpose include the following:
REMA and Rwanda Meteorological Service staff expertise and education and training backgrounds
Characterization of severe flooding events (time- and spatial-scale, seasonality, vulnerable catchments)
Current and anticipated meteorological and hydrologic instrumentation (numbers and locations, maintenance and calibration schedules, period-of-record)
Weather-related hazard mitigation priorities Land-use, soil-types, and, anticipated population demographics, and other environmental
considerations Infrastructure for technological development
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In collaboration with REMA staff, we will plan the site visit so that our team will be able to visit critical sites (i.e., the Gishwati ecosystem study area), visit instrumentation sites and computing facilities, and meet with the appropriate officials and stakeholders in our time in-country.
Task 2: Conduct Site Visit Capability AssessmentFollowing the examination of background materials the NCAR team will send a group of 4-5 scientific and technical experts to Rwanda for direct meetings with REMA and those persons at the national and regional/local levels who will be involved in the project. It will be important for us to understand the current infrastructure and organization for providing forecasting services and to get to know the people with whom we will be working. In Kigali, we would expect to visit any centralized forecasting, computing, or information technology/data processing facilities. We would then visit the Gishwati watershed to begin to understand this unique ecosystem and its specific hydrological, land surface, and atmospheric conditions. We would plan to visit any meteorological instrumentation in the area, review any records that may be kept, and meet with individuals who know the region well.
Subject to discussions and guidance from REMA, the site visits will include consideration of the following aspects of the current hydrometeorological capabilities:
Surface observational systems Upper air soundings (radiosondes) Satellite data reception and processing Communication networks (both WAN and LAN) and technologies for data transfer and
exchange between Rwandan facilities Use of numerical models Meteorologist workstations and internet access Age, condition, and adequacy of aeronautical meteorology instruments and equipment Age, condition, and adequacy of the existing support infrastructure (buildings, working
space) Staffing, training, maintenance, and procedures Potential environmental hazards or concerns
Needs AssessmentThe second important component of the assessment is aimed at understanding the needs of REMA and its key stakeholders (i.e., those who have a strong interest in the outcome of the project). Working closely with officials and stakeholders we will discuss the following topics, as well as others that emerge in conversation:
What are the most critical time-scales for forecasts (e.g., hours, days, weeks, seasons)? What are the most pressing infrastructure needs (e.g., instrumentation, computing,
networking, data processing)? What would a useful early warning system look like? What should it do and how should it
work? How will warnings be communicated to the public? Are there improvements in public
awareness and methods of disseminating information that need to be made? What do forecasters in Rwanda need with regard to education and training to take
advantage of new technologies, facilities, and systems?
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Task 3: Analyze Results of Site Visit and Prepare Report The NCAR team will synthesize the data acquired from REMA, stakeholders, and the site visit, and continue to engage with the sponsor in asking questions, exchanging information, and ensuring that our analysis considers changes or new developments. If the Government of Rwanda pursues the acquisition of major new facilities such as a radar or precipitation gauges, we would clearly want to consider how those purchases could most effectively improve current forecast capabilities.
The final step under this task is the preparation and delivery of a working paper discussing the findings of the assessment effort. These findings will form the basis for the recommendations NCAR provides to REMA for moving forward with a Phase 2 major implementation effort.
2.2 Utilization of publicly available data sources
Our response to this call is to first provide known technologies that are currently and publicly-available without reliance on incomplete in situ measurement networks and research developments, while allowing utilization of further data resources as Rwanda’s current EWS infrastructure continues to mature and move into specific Phase 2 developments (highlighted in the “On the Terms of Reference” document).
Current public data sources addressed in this proposal include satellite precipitation estimates and global NWP weather forecasts. Each of these is discussed briefly below. It must be stressed that the skill of these estimates and forecasts depends on many factors: the temporal and spatial response scales, the predictability of the weather and surface hydrologic systems involved, and specific user needs. It may be unrealistic to expect that these large-scale data products will have high accuracy at the space and time scales at which flooding occurs in the fast-response catchments in Rwanda. As such, these large-scale publicly available data products are primarily used as context for the subsequent development and interpretation of local-scale forecasting products developed in future phases of the program.
Importantly, we propose to provide verification and evaluation of these current resources to determine the spatial and temporal scales for which they may have potential to provide utility to address regional predictability needs. Further we stress that new EWS advanced infrastructure being pursued under different proposal calls (e.g., rain gauges and computational cluster installation) should significantly enhance the possibilities for EWS skill, and these capabilities be utilized in future phases of the work. The purpose of this proposal, then, is to lay a foundation for these future applications. Data resources discussed below in this beginning Phase 1 will focus on satellite-based precipitation estimation and NWP at medium-range spatial and temporal scales, which have limits on their predictive capacity for Rwanda’s river basins, some of which are shown below in Figure 2.
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Fig. 2: Map of Rwanda showing significant rivers.
2.2.1 Satellite-based precipitation estimation Precipitation products from three satellite-based sources will be investigated for their range of spatial and temporal scales for which they may possess remotely-sensed observational capacity relevant to EWS system development. These sources are: the NASA-TRMM 3B42 product, the NOAA Hydro-Estimator (H-E), and NOAA-CMORPH. H-E’s benefits are its rapid refresh capacity (hourly rates, 15-min latency), as compared to the longer-latency NASA-TRMM and NOAA-CMORPH products. However, by relying on the infra-red spectrum (IR) solely, the H-E is less accurate than the TRMM/CMORPH products, which merge passive and active microwave remotely-sensed signals with IR through distinctly-different algorithms. As a result, potential hydrometeorological applications of these resources to update, say, regional soil moisture conditions, should be provided by the most recently-available data set (IR-based), with a phased introduction of the merged products (TRMM/CMORPH) as they become available.
2.2.2 Thorpex-Tigge Medium-range Weather prediction This project would make use of the recently available THORPEX Interactive Grand Global Ensemble (TIGGE: http://tigge.ecmwf.int/) multi-center ensemble weather data, with focus on ensemble fields that could inform extreme precipitation predictability (see: http://tparc.mri-jma.go.jp/TIGGE/tigge_extreme_prob.html). We stress that precipitation field prediction alone is often highly limited in skill, particularly at the scales of small river catchments. For this study, we propose to investigate regional dynamical features potentially captured within a selection of weather fields contained in this archive, and how their diagnosis and predictability could potentially be utilized to inform precipitation predictability.
2.3 Information Management and Decision Support Systems (DSS) for Weather and Flood Forecasting
The variety of infrastructure and capability improvements undertaken under a weather and flood forecasting program (Phase 2), whether for the Gishwati region, or a further country-wide coverage extension, will result in large amounts of new data. Maximum benefit can be drawn from those data
with the appropriate use of information management and decision support tools. Expected new data that would be generated in support of such forecasting efforts would come from the following sources: (1) real-time hydro-meteorological monitoring systems such as Automatic Weather Stations (AWS), rain gauges, streamflow stations, and satellite sensors; and (2) numerical weather prediction model forecasts (NWP) of future meteorological conditions, at different lead times ranging from days to seasons and (3) numerical hydrological prediction of future rainfall and runoff over the basins of interest and at different lead times. Each of these data sources will require processing, storage, and retrieval. Decision support tools will incorporate each of these data sets in a manner which improves their understanding and facilitates decision-making by managers and policymakers.
2.3.1 Information Management An effective information management system integrates processed weather forecast fields and the other various data sources into a single information management framework. This framework is then used to produce combined alerts, support a common visualization tool, populate a single decision support system, and/or feed data to additional forecasting and analysis tools. The number of data sources to integrate and the optimal depth to which they would be integrated will be dependent on the availability of the data sources, their spatial and temporal dimensions, their criticality to downstream modeling and visualization systems, and the final user-determined needs of the overall system. NCAR has developed and employed a wide variety of DSS for weather and flood forecasting, most recently in Bangladesh and Romania.
2.3.2 Standardized Formats and Interfaces Information Management is greatly facilitated when data are available to all components via “on the wire” standards. “On the wire” standards means that all the data sources (repositories, aggregators, replicators) expose the data using a standard which is also supported by all of the data consumers (DSS, aggregators, replicators, processors, and visualization). Using a common protocol for communication and data exchange between the various components provides for maximum flexibility in assembling data networks. Flexibility, then, allows data sources, repositories, models, and decision support tools to be connected in novel ways for maximum benefit.
The meteorological research community has used the NetCDF format for many years to exchange and store data. Recently, the “CF” convention of NetCDF has been adopted by an international standards body, the Open Geospatial Consortium (OGC), as a standard format for gridded coverage and feature-based geospatial information. Whenever possible, this format will be used for exchanging data. When it is not feasible to use NetCDF, due to lack of support by the data producer or consumer or due to deficiencies in the format’s ability to represent desired information, another format may be employed. Other common formats supported by existing meteorological tools include WMO standards, GRIB, BUFR, MDV, GML, and Geo-JSON.
The OGC standards body has developed a set of standard request and response protocols for geo-spatial data: Web Coverage Service (WCS), Web Feature Service (WFS), and Web Map Service (WMS). These protocols operate over internet-standard HTTP, HTTPS, and TCP/IP protocols. They guarantee that a data consumer will be able to formulate a request to a data provider, and that that request will result in an understandable response. WCS is tailored to gridded coverage data, WFS is tailored to feature (point or polygon) data, and WMS is tailored to producing presentation map imagery. Whenever possible, one of these formats will be used for requesting and serving data. When they are not feasible to use, due to lack of support by the data server or consumer, another protocol supported by both consumer and producer may be substituted.
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2.3.3 Data Repository and Replication Data repositories are an essential part of the information management system. A centralized repository, in the form of a database or filesystem store with data services, would facilitate data access by consumers and would be simple to manage and replicate, but may not provide the desired level of data throughput, network accessibility, and fault-tolerance desired for a national information management system. A distributed respository in the form of geographically disparate data aggregators and replicators would provide more scalability, resulting in greater data throughput, network accessibility, and fault-tolerance. A distributed data repository network would also require significantly more effort to install and manage. Given the initial scope of this program, it is recommended to aggregate data in a centralized repository whenever possible. This repository will be hosted on a single computer or connected nodes and will provide coverage and feature data access to consumers using the standard protocols described earlier. Data replication and archiving will be performed on a parallel system, as necessary. The data repository will be initially developed and run experimentally at NCAR, which will then be transferred to an operational system in the field. The client will provide staff with sufficient skill in network administration and system configuration to manage the installation and maintenance of the operational system. Future phases of development may include extension of the initial information management system into a distributed network.
2.3.4 Decision Support An integrated Decision Support System (DSS) will be developed to provide managers and policy-makers at many levels with the data they need to make informed decisions. The different users of the system may require subtly different presentations of data to support their decision-making, such that it may not be possible to create a single presentation that meets the needs of each user. For the initial phase of the program, a single tool will be developed to meet a majority of each user’s needs. As much as possible, the data serving system will be developed in such way that it can support additional tailored tools which may be developed in future phases.
The initial decision support tool will show data in two primary ways: as raw data and as synthesized information. At a raw data level, the DSS will expose the data produced by each data source in a manner which is close to its native data format. Observation values from meteorological sensors will be presented at geo-located points with minimal unit conversion and formatting. Gridded satellite and model fields will be presented as graphical color-coded overlays on the map. At a data synthesis level, the DSS will present additional views of the data which may further facilitate decision-making. Observations may be presented in a spatial or temporal context which conveys geographic extent or time trending. Gridded data may be shown in 2-dimensional slices through 3 or 4 dimensional data, such as cross-sections. Fields may be composited and/or
interpreted to produce alerts over areas and times. The figures below show examples of DSS developed by NCAR.
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The decision support tool will connect to data services over the internet. It will run on a standard desktop computer with any of the leading contemporary operating systems, Windows, Macintosh, and Linux. The tool will be accessible from anywhere on the internet, either as a Java application or a browser-based web application. It may be restricted to specific users by IP address or login, as determined. Tool performance will be dependent on the network speed.
2.3.5 Data Services Data services will be run on the same host as the data repository. Services will be available over
the open internet to support access by the decision support system tool and to provide for future use by other tools and systems. The proposed system will be initially developed and run experimentally at NCAR, and will then be transferred to an operational system in Rwanda. The Rwanda system will require staff with sufficient skill in network administration and system configuration to maintain the operational system.
2.4 Training
While the decision support tools and hardware/software systems developed at NCAR are important in themselves, the education and training of stakeholders and end users is essential for the products to have their full impact. We typically invest considerable time and energy in conducting workshops and tutorials, developing and delivering tailored instructional materials, and bringing visitors to NCAR for collaborative visits. Each year hundreds of scientists from around the world attend workshops at NCAR focused on use of the Weather Research and Forecasting (WRF) model, the Community Climate System Model (CCSM), and the Model Evaluation Tools (MET). Numerous other workshops bring scientists together to share their knowledge and develop new research agendas in areas such as climate, weather and health; water resources; verification of weather forecasts; and numerical weather prediction and data assimilation.
In terms of specific training anticipated and tailored for REMA and the Rwandan Meteorological Office personnel, in Phase 1 it is broken down into three goals: (a) in the early period of performance (PoP) of the consultancy, training will be provided on the basic technologies the contract will introduce to REMA; (b) at the end of the PoP, the goals will be to provide additional training on new technologies introduced, revisiting the theory introduced in (a) but focusing on operational and functional issues; and, (c) holding a workshop on proposed future phases of EWS technologies to be introduced, which would include basic science tutorials along with dialogue with Rwanda participants on local needs, tailoring, and expectations.
Regarding (c), it is worth noting again that the need to provide training on possible future EWS technologies comes about because NCAR is proposing that REMA develop its EWS capability in phases. In particular, this means in the future a strong dependence on advanced observational data (i.e., radar, rain gauge, satellite products, etc). Trainings sessions on new technological developments would be kept general enough that if REMA were to use a different institutional consultant for latter EWS development, the materials would still be relevant.
An example of training provided to bridge from a “Phase 1” to a “Phase 2,” EWS capability would include advanced short-term storm forecasting (termed nowcasting). A number of technologies developed at NCAR would be relevant in this effort including TITAN, a storm tracking system that obtain storm motion and storm growth trends; heuristic nowcasting (e.g., the NCAR Autonowcaster), and statistical optimization (e.g., genetic algorithms, and “random forest” methods) to predict the development and evolution of storms; blending of numerical model
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forecasts with nowcasts, including model phase error correction, and techniques to combine all this information into a single consistent forecast picture with uncertainty bounds. Techniques have also been developed at NCAR to use satellite-based rainfall estimates in conjunction with global numerical weather prediction results to produce timely, high-resolution 0-3 hr nowcasts of rainfall amounts. This product was developed for use in remote (often oceanic) regions where surface-based radar networks are not available. Techniques to blend these rainfall estimates with NWP results for the purpose of extending the forecast period to 12 hr are underway now and could be implemented as an extension to the 0-3 hr product, once the blended product is proven to have adequate performance.
3.1.1 Tasks Gishwati region infrastructure assessment – 2 days National infrastructure assessment: radar, DSS and dissemination –4 days Pre-project training on capacities and considerations – 2 days
3.1.2 Budget Pre-trip planning and survey, 2 week trip (5 personnel), future technical development plan and recommendations completed (17 person-weeks)
3.1.3 Timeline Completion date: no later than 6 months from start of project
3.2 Utilization of publicly-available large-scale datasets
Context: it is presumed at present that the only currently available data relevant for early warning system deployment will exclusively come from publicly-available data sources, specifically: satellite precipitation estimates, medium-range numerical weather prediction from global modelling centers (e.g., NCEP, ECMWF, etc.)
The data sources are (1) satellite precipitation estimates (SPE), including the NOAA Hydro-Estimator, NASA TRMM, NOAA Cmorph; (2) any in-situ in-country rain gauges; and (3) freely-available GCM ensemble forecasts (NCEP, Thorpex-Tigge archived data)
3.2.1 Tasks GIS delineation of sub-catchments within the Gishwati region of the pilot program (1
person-wk) Processing of any-available raingauge and/or streamflow data sets (2 person-wk) Processing of past and current SPE over pilot study area (1 person-wk) Processing of past and current NWP over pilot study area (2 person-wk) Skill assessment of SPE as compared to any available rain gauge (1 person-wk)
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Skill assessment of NWP as compared to any available rain gauge or SPE (1 person-wk) Post-processing algorithm development of SPE and NWP to application (2 person-wk) Hydrologic modelling development and comparison studies (8 person-wk) Automation of SPE and NWP download and processing (2 person-wk) Automation of integrated data sets into rudimentary hydrologic variables and forecasts (2
person-wk) Assessment of storm climatologies and characteristics (6 person-wk)
3.2.2 Budget 28 person-weeks
3.2.3 Timeline Completion date: 12 months from start of the project.
3.3 Information Management and Decision Support Systems (DSS) for Weather and Flood Forecasting
Context: consultant assumes that in-country data sources for DSS ingest will be available over open-internet or secure private network (VPN) in a WMO-approved format.
3.3.1 Tasks Display currently available satellite and numerical weather prediction products (4 person-
wk) Build capacity for future integration of met sensors, radar, derived variables (hydromet),
tasking entailing ingest, preprocessing and storage, data services for disseminationo Radar (8 person- wk)o Raingauge (6 person-wk)o Streamflow measurements (6 person-wk)
GIS situational awareness, including base maps and tools (4 person-wk)
3.3.2 Budget 38 person weeks
3.3.3 Timeline Information development for currently available products and DSS development: beginning
of POP ongoing In-country hydromet sensors: beginning once data are available
3.4 Training
3.4.1 Tasks In-country training covering existing technologies contained within the scope of this proposal, along with training on possible anticipated technologies relevant to EWS (a more in-depth continuation of short lectures provided at beginning of consultancy).
3.4.2 Budget 1 week trip, 4 personnel, preparation, travel, and content delivery (4 X 2 wk = 8 person weeks)
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3.4.3 Timeline Training near the end of the project period of performance (18 month)
