Observing System Simulation Experiments at CIMSS

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Observing System Simulation Experiments at CIMSS. By CIMSS/OSSE Team : Bob Aune ; Paul Menzel ; Jonathan Thom Gail Bayler ; Chris Velden ; Tim Olander and Allen Huang Cooperative Institute for Meteorological Satellite Studies University of Wisconsin 7 June, 1999. Road Map. - PowerPoint PPT Presentation

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Observing System Simulation Experiments at CIMSS

By

CIMSS/OSSE Team :

Bob Aune ; Paul Menzel ; Jonathan Thom

Gail Bayler ; Chris Velden ; Tim Olander

and Allen Huang

Cooperative Institute for Meteorological Satellite Studies

University of Wisconsin

7 June, 1999

MESOSCALE OBSERVINGSYSTEM SIMULATIONEXPERIMENTS (OSSE)

PURPOSE

To assess the value of environmentalobserving systems to operationalmesoscale numerical weather forecasts in acontrolled software environment.

Future observing systems can be testedusing projected instrument characteristics.

Road Map

1) Geostationary Interferometer Initial Focus

Soundings (S) = T, Td

Winds (W)

Soundings plus Winds

Soundings, Winds plus Conventional Data (CD)

2) First: Simulated Products; Followed by Derived Products from Simulated Radiances

3) Investigation of LIDAR Winds on top of S, W, and CD

PROCEEDURES

Observations are synthesized from forecasts generated bya numerical prediction model that has a known historycalibrated against reality.

These forecasts represent truth and are referred to as the"nature" atmosphere.

Synthesized observations must mimic, as close aspossible, observations from the real observing system thatis being evaluated.

Synthesized observations are assimilated into anassimilation system that is independent of the "nature"model.

This OSSE is being conducted over a limitedarea domain. The assimilating forecast modelmust be isolated from the influence of pre-specified lateral boundaries.

Pilot Experiment

HYPOTHESIS:

Information from a geostationary-basedinterferometer will significantly improve theaccuracy of numerical weather forecasts overthe current geostationary radiometer.

OSSE Design

An OSSE can be subdivided into four basic steps:

1) Generate a "nature" atmosphere

2) Compute synthetic observations

3) Assimilate the synthetic observations

4) Assess the impact on the resulting forecast.

Each step is performed with the goal of minimizingany external influences, which may compromise thevalue of the synthesized observations, theassimilation process, or the results of the numericalforecasts.

1. Generate "Nature" Atmosphere

MODEL: University of Wisconsin, Nonhydrostatic ModelingSystem (UW-NMS).

HORIZONTAL DOMAIN: Large as practical to isolate theinfluence of pre-defined lateral boundary conditions.Horizontal resolution = 60 km.

BOUNDARY CONDITIONS: NCEP Eta forecast model,AWIPS 212 grid.

NOTE: Ideally, the "nature" atmosphere should be two tofour times the resolution of the simulated observingsystem.

VERTICAL RESOLUTION: a minimum of two-times theresolution of the observing system to be simulated.Vertical levels = 38.

INITIALIZATION: 12hr forecast "spin up".

UW-NMS Domain in the Eta 104 grid

OSSE Control (Nature) Verification

OSSE Control (Nature) Verification

OSSE Control (Nature) Verification

2. Simulate observations

Temperature and moisture profiles from the "true"atmosphere are modified using realisticobservation errors.

Profiles of temperature and moisture are generatedat hourly intervals over the 12-hour analysis period.

A cloud mask is used to simulate gaps in thecoverage.

Simulated Error for TemperatureGEO-I GEO-R

0

100

200

300

400

500

600

700

800

900

1000

1100

0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5

Degrees C

Pre

ssu

re h

Pa

3. Assimilate Synthesized Observations

The operational 40km Rapid Update Cycle (RUC) wasused to assimilate the observations at hourly intervals.

Boundary conditions: NCEP Eta model, projected ontothe AWIPS 211 grid (80km resolution).

Four assimilation experiments were performed:

1) No observations (NO)

2) Perfect observation experiment (PO) assimilates profilesextracted directly from the "nature" run

3) Geostationary radiometer (GEO-R) experimentassimilates profiles adjusted to emulate a GOES-typesystem.

4) Geostationary interferometer experiment (GEO-I)assimilates profiles from a proposed geostationaryinterferometer.

Note: The NO and PO experiments represent the range ofperformance that can be expected from the RUC.

4. Assess The Impact

The impact of the observations will be assessed by objectively measuring the ability of each observing system to steer the resulting 12-hour forecasts toward the “true” atmosphere.

Observation count

T/Td* WIND# Experiment

None None No Observations (NO) = NO OBS

~5000 ~6000 Geo. Radiometer (GR) = GEO-R

~5000 ~6000 Geo. Interferometer (GI) = GEO-I

~6200 ~12000 Perfect Observations (PO) = OPTIMAL

* : profile # : vector

Preliminary Results

GEO-I results are significantly improved over thosefrom the GEO-R.

500 hPa temperature errors are reduced by 0.2 C rootmean square (rms) over the extended CONUS(contiguous United States) and 700 hPa relativehumidity errors are reduced by 2%.

To assess the impact of the geostationaryinterferometer over the geostationary radiometer arelative score (1 to 10) was computed.

The RMS errors for temperature and relative humiditywere summed over four layers (700hPa, 500hPa,400hPa, 300hPa) and normalized between the RMSerror sums from the No Observation (NO) run and thePerfect Observation (PO) run. A score of 10 is perfect.

What’s Next?

COMPLETE PILOT STUDY

Cloud track and water vapor winds (IR and interferometer)

Add conventional observing systems

FUTURE DIRECTIONS

High resolution UW-NMS "nature" forecasts

Independent boundary conditions

14 day test periods (winter and spring)

Radiance assimilation (3D-Var)

Low Earth Orbit (LEO) OSSE

Wind Objectives

1) To assess the value of wind observations to operational mesoscale numerical weather forecasts in a controlled environment

2) To assess the incremental value of current and future geostationary infrared/visible, and LIDAR wind measurements

Wind Profile OSSE

Simulated Error for Clear Water Vapor Winds (m/s)

Pressure (mb) GEO-R GEO-I*

200 N/A 4

300 6 4

400 5.5 3.5

500 5 3

700 N/A 2.5

* Projected 30-40 % improvement over Geo-R

Wind Profile OSSE Simulated Error for Cloudy Water Vapor & IR

Winds (m/s)Pressure (mb) GEO-R GEO-I*

200 5.5 3.6

300 5 3.3

400 4.5 2.9

500 4.5 2.9

700 4.0 2.6

850 3.5 2.3

* Projected 30-40 % improvement over Geo-R

Cloud altitude defines wind level

Winds OSSE Results

Winds OSSE Results - Continued

Winds OSSE results are preliminary

Winds Versus Soundings

Winds Versus Soundings - Continued

Current Status

1) OSSE is designed, implemented, and pilot experiment conducted.

2) Winds counterpart, sounding profiles, are successfully assimilated and forecast impact assessed for a case study.

3) Winds assimilation is performed and is under detailed analysis now.

4) CIMSS OSSE home page:

http://cimss.ssec.wisc.edu/model/osse/osse05.html

Plans for the On-going Work

1) Simulate winds using radiances

2) 14 day test periods (winter and spring)

3) Conventional observing system evaluation

4) Low Earth Orbit (LEO) OSSE

5) Temperature/water vapor sounding and winds combined OSSE

6) LIDAR wind OSSE

Hurricane Bonnie Wind and Cloud Fields

Wind Vectors :

Red - 1 km level

Green - 14 km

level

Clouds :

Light gray -

Ice Cloud

Dark Gray -

Water Cloud

Hurricane Bonnie Wind and Cloud Fields

Wind Vectors :

Magenta

Stream Lines :

Green (@ 4 km)

Clouds :

Light gray -

Ice Cloud

Dark Gray -

Water Cloud

GOES Radiances Simulation Verification

Wind Tracking Verification

Wind Tracking Verification - Continued