observing system simulation experiments at cimss
DESCRIPTION
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 PresentationTRANSCRIPT
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