RT Modelling for All-Weather Microwave Radiance Assimilation
Toward All-Sky Assimilation for Microwave Radiances (Part II)
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Transcript of Toward All-Sky Assimilation for Microwave Radiances (Part II)
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Toward All-Sky Assimilation for Toward All-Sky Assimilation for Microwave RadiancesMicrowave Radiances
(Part II)(Part II)
1,21,2Min-Jeong KimMin-Jeong Kim
11Emily Liu, Emily Liu, 11Yanqiu Zhu, Yanqiu Zhu, 11Daryl Kleist, Daryl Kleist, 11Andrew Collard, and Andrew Collard, and 11John DerberJohn Derber
and and
1. 1. NCEP/EMCNCEP/EMC
2. CIRA/Colorado State University2. CIRA/Colorado State University
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• Introductions to our current all-sky radiance DA system and its current status were presented in the previous talk by Emily Liu.
• This talk will focus on assessment of the system to diagnose issues and to build strategies to improve.
• In addition, roles of GFS moisture physics schemes in all-sky radiance data assimilation and impacts of using them on GFS model forecasts will be presented.
• Ongoing work and outstanding tasks will be addressed.
Outline
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GFS Forecast Impact Experiments
• Ensemble-3DVAR Hybrid GSI (operational)
• Test resolution: T254 (Note: NCEP operational resolution is T582)
• Test period: 07/01/2012 – 07/31/2012
#1 other instrument observations + Clear sky AMSU-A (operational GSI)
#2 other instrument observations + Clear sky AMSU-A + cloudy sky AMSU-A (without moisture physics)
#3 other instrument observations + Clear sky AMSU-A+ cloudy sky AMSU-A (with moisture physics)
Experiments:
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SH Geopotential Height AC (500 hPa)
Control (clear sky radiance DA)
ECMWF obs. error model
Estimate obs. Error from GFS O-F
Control
• For now, assimilating cloud affected AMSU-A radiances making slight positive impacts on Southern Hemisphere.
• Neutral impacts elsewhere..
GFS Forecast Impact Experiments
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Assessments (Part I)
Clear sky AMSU-A (Operational GSI)
vs.
Clear+Cloudy Sky AMSU-A (No Moisture Physics Used.)
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CNTL: Clear sky DATEST: Clear + Cloudy Sky (Cloud microphysics not used), Obs.err estimated from cycled results
GFS Model 24 hr Forecast Errors: 500 hPa Geopotential HeightGFS Model 24 hr Forecast Errors: 500 hPa Geopotential Height
Allsky Error - ClearSky Error
Regions with positive impacts (near sea ice edge..)
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CNTL: Clear sky DATEST: Clear + Cloudy Sky (Cloud microphysics not used), Obs.err estimated from cycled results
GFS Model 24hr Forecast Errors: 500 hPa TemperatureGFS Model 24hr Forecast Errors: 500 hPa Temperature
Allsky Error- ClearSky Error
Regions with positive impacts (near sea ice edge)
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CNTL: Clear sky DATEST: Clear + Cloudy Sky (Cloud microphysics not used), Obs.err estimated from cycled results
Analysis Increment: 500hPa TemperatureAnalysis Increment: 500hPa Temperature
All Sky Clear Sky
AllSky - ClearSky
Compared to results from operational GSI, all-sky radiance assimilation system increases atmospheric temperature near 60°S
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CNTL: Clear sky DATEST: Clear + Cloudy Sky (Cloud microphysics not used), Obs.err estimated from cycled results
Analysis Increment: Total Column Cloud WaterAnalysis Increment: Total Column Cloud Water
All Sky Clear Sky
AllSky - ClearSky
Compared to results from operational GSI, all-sky radiance assimilation system makes lots of clouds near 60°S
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• We are currently applying cloudy radiance data assimilation system in ocean surface only.
• It seems like some parts of sea ice edge are defined as ocean in the current system. Therefore, we see observations with warm signal from sea ice are split into (1) increasing atmospheric temperatures(2) generating clouds
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Assessments (Part II)
Cloudy sky AMSU-A without moisture physics
vs.
Cloudy sky AMSU-A with moisture physics
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GFS Model Moisture Physics
Deep convection scheme
Shallow convection
scheme
Grid-scale condensation
scheme
Precipitation scheme
Surface rain rates
cw, T, q profiles
TL/AD codes are completed and currently being tested.
Not yet developedNeeds debugging
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Moisture physics (TL,AD)
Inner loop
Outer loop
CRTM
Role of GFS Moisture Physics in All-Sky MW Radiance DA
1. Generating clouds even when we don’t have clouds in background..
2. Ensuring balance between water vapor and clouds. (e.g. prevent generating clouds in dry environment.)
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• All-sky AMSU-A included.
• Setting no clouds in the background.
• Including “moisture physics(mp)” in the inner loop.
Clouds are generated using the information from observations (wv cw)
Role of GFS Moisture Physics in All-Sky MW Radiance DA
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With Moisture physics Without Moisture physics620hPa
CW increment
Water Vapor increment
RH >80 % in analysis
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GFS Model 24 hr Forecast Errors: 500 hPa TemperatureGFS Model 24 hr Forecast Errors: 500 hPa Temperature
CNTL: Clear + Cloudy Sky (Cloud microphysics not used)TEST: Clear + Cloudy Sky (Cloud microphysics used)
Error(MoistPhy)- Error(Without MoistPhy)
Currently, including moisture physics increase temperature forecast errors especially near the Tropics.
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CNTL: Clear + Cloudy Sky (Cloud microphysics not used)TEST: Clear + Cloudy Sky (Cloud microphysics used)
Analysis Increment: Total Column Cloud WaterAnalysis Increment: Total Column Cloud Water
With MoistPhy Without MoistPhy
With MoistPhy - Without MoistPhyClouds are generated less when using moisture physics.. •Good to see that near the sea ice edge •But maybe making not enough clouds near Tropics .. ?•Increasing water vapor instead of clouds near Tropics ..??
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CNTL: Clear + Cloudy Sky (Cloud microphysics not used)TEST: Clear + Cloudy Sky (Cloud microphysics used)
Analysis Increment: Total Column Water VaporAnalysis Increment: Total Column Water Vapor
With MoistPhy Without MoistPhy
With MoistPhy - Without MoistPhy Using moisture physics in the optimization increases water vapor near Tropics
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Summary
- We starts to see our all-sky microwave radiance system makes slight positive impacts on Southern Hemisphere.
- We need to improve the way discriminate sea ice from ocean surface for all-sky radiance assimilation
- Moisture physics degrading results especially near the Tropics for now. It shows the tendency evaporating clouds maybe too much. Further investigation is ongoing.
202020
Ongoing & Future Plans
2020
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Systematic Bias Increasing with CLWPSystematic Bias Increasing with CLWP
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How to deal with precipitation we couldn’t removed from QC
(1) Revisit QC to remove moderate/lightly precipitating observations
(2) Enhance radiance bias correction scheme for all-sky microwave radiance assimilation See Yanqiu Zhu’s presentation tomorrow
(3) Include precipitation in the background fields
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Revisit Quality ControlRevisit Quality Controlfor cloud affected radiance data in “non-precipitating” skyfor cloud affected radiance data in “non-precipitating” sky
Evaluating QC using Observed (retrieved) TMI surface rainrates
Detecting “heavy” rain with scattering
Missing“moderate/light” rain without scattering
not assimilatedassimilated
15211 454.0)0049.0241.2( chchchch TBTBTBTBdsval 2
6
2
8.0106
chTBdsval
factch factch6 > 1.0 : precipitating in operational GSI
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How to deal with precipitation we couldn’t removed from QC
Deep convection
scheme
Shallow convection
scheme
Grid-scale condensation
scheme
Precipitation scheme
Surface rain rates
cw,t, q profiles
GFS moisture physics
Brad Ferrier kindly helped us calculate the rain and snow mixing ratio profiles using GFS large-scale precipitation scheme
(1) Revisit QC to remove moderate/lightly precipitating observations
(2) Enhance radiance bias correction scheme for all-sky microwave radiance assimilation See Yanqiu Zhu’s presentation tomorrow
(3) Include precipitation in the background fields
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Cloud water error statistics show “non-Gaussian” distribution
Moisture Control VariablesMoisture Control Variables
Available options for moisture control variables in GSI
• cw – Currently being used
• rhtot – Needs work for Ensemble side
• cw/σcw – Set up in GSI. Needs to build background error covariance for global analysis
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Thank you !
Any comments or questions?
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BACKUP SLIDES
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1. Screening Precipitating sky:factch6(>1) for all surface type : screening ch1-6, 15Based on scattering index (sval) and channel 6 O-F
2. Screening thick cloudy sky:Screening thick cloudy sky: factch4(>0.5) for all surface type: screening ch1-5, 15Based retrieved cloud liquid water path and channel 4 O-F 3. Sensitivity of Tsim to Surface emissivity for ocean surface
4. Topography : inflating error for ch 6 (z>2km) or ch 7(z>4km)
5. Transmittance
6. Inflated near Tropics ( 0.75 at 0°, and 1 at 25°N & 25°S)
Cloud affected radiance DA
Quality ControlQuality Control
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Scan angle dependence
Observation Error Varying with AMSU-A Scan Angle
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AMSU-A Cloudy Radiance Data
METOP-A CH 2 TBs
Hurricane Sandy (10/28, 00Z, 2012)
Remove precipitation & thick clouds
QC in “operational” GSI
Remove precipitation KEEP thick clouds
QC in all-sky GSI
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