Observation Impact on Mesoscale Model Forecast Accuracy over Southwest Asia

© The Aerospace Corporation 2014

Observation Impact on Mesoscale Model Forecast Accuracy over Southwest Asia

Dr. Michael D. McAteeEnvironmental Satellite Systems Division (ESSD)User Applications and Integration (UA&I)

ESSD/UA&IFebruary 2014

Approved for Public Release – Distribution Unlimited

Aerospace Corporation

Mesoscale or "middle" scale model refers to Meteorological Forecast Models the have a resolution/grid space ~ 10 -100 km that cover only a limited area.

[email protected]/UA&I

Overview

• Observation Impact Assessment Tool History and Description

• System Configuration for Southwest Asia

• Impact Results– Select day– Period Averages

• Summary and Future Work


Impact Tool History and Description

• Based on results presented in a JCSDA newsletter, AFWA16th WS personnel were encouraged to pursue the development of an observation impact assessment tool based on techniques developed at the Naval Research Laboratory (NRL)

• AFWA tasked the National Center for Atmospheric Research (NCAR) with developing such a tool that could be used with its operational forecast model and data assimilation system (WRF and WRF DA) leveraging previous work that had been done to develop 4DVAR

• Aerospace working with NCAR, AFWA and its contractors installed the system in AFWA’s development environment

Forecast Sensitivity to Observations


Joint Center for Satellite Data Assimilation (JCSDA)


Weather Research and Forecasting (WRF) modelWeather Research and Forecasting model Data Assimilation (WRF DA) system


4-Dimensional Variational (4DVAR)


Impact Tool History and Description

• The tool, which is called Forecast Sensitivity to Observations (FSO),has the ability, using adjoint techniques, to quantitatively estimate the impact that assimilating observations has on short-range WRF model forecast accuracy

• The FSO system used in this study consists of WRF DA, WRF, the adjoint to WRF, and the adjoint to WRF DA

• An FSO capability has been developed to work with the Gridpoint Statistical Interpolation (GSI) system



The Case for FSO?

• Meets a need to know not only the impact of observations but also their relative value

• A single run of FSO can provide the relative value of all observations assimilated without the need for multiple and computationally expensive with- and without- model runs

• FSO can provide the critical information needed to intelligently select which channels from space based remote sensors will be assimilated

• FSO can be used to monitor the health of an NWP center’s data assimilation system as well as the health of the observations it uses

• FSO can determine which individual observations improved or degraded the forecast



Numerical Weather Prediction (NWP)


So How Does FSO Work?

• Non-Linear (NL) forecast models can be linearized (with simplifications)

• The resulting Tangent-Linear (TL) model represents the linear evolution of small perturbations

• The mathematical transpose of the TL code is called the Adjoint (ADJ) and it transports sensitivities back in time

• The ADJ of the data assimilation system is needed to compute the sensitivity to observations. It can be computed with various methods but the one used in FSO is the Lanczos minimization (Fisher 1997, Tremolet 2008)



Implementation in WRFObservation

(y)WRF-VAR

Data Assimilation

WRF-ARWForecast

Model

Forecast(xf)

DeriveForecastAccuracy

Background(xb)

Analysis(xa)

Adjoint of WRF-ARW

ForecastTL Model (WRF+)

ObservationSensitivity(F/ y)

BackgroundSensitivity(F/ xb)

Sensitivityat the Initial

Time(F/ x0)

Observation Impact<y-H(xb)> (F/ y)

Adjoint of WRF-VAR

Data Assimilation

Obs Error Sensitivity(F/ eob)

Gradient of F

(F/ xf)

DefineForecastAccuracy

ForecastAccuracy

(F)

Bias CorrectionSensitivity

(F/ k) Figure from NCAR which was adapted from Liang Xu at NRL


FSO Configuration

• AFWA SW Asia Domain (T4)• WRF, WRF DA Ver 3.2.1

– 45 km grid spacing– 57 levels, model top 10 mb– Limited data assimilation (DA) cycle– DA Cycle times: 00,06,12,18 UTC

• 24 hour forecast length• Dry energy forecast error metric

• Impact computed for sub region • Study period 1-29 January 2012 for 00

and 12 UTC cycles

Forecast Error Sensitivity wrt

Potential Temperature

(oK)

x,x 12

[ u 2

v 2

gN

2

2 1

cs

2

p 2 ]d F=


Theta is Potential Temperature


FSO Configuration

• Aircraft Reports(AIREPS)• Radiosondes (Sound)• Feature Track Winds (GeoAMV)• SSMIS Retrievals (SSMIS_RV) of

Total Precipitable Water (TPW) and Ocean Surface Wind Speeds(OSSW)

• Surface observations (METAR, Synop)

• GPS Refractivities (GPSRF)• Ships and Buoys• SSMIS brightness temperatures for

select temperature and humidity “sounding” channels (SSMIS)*

AIREPS Sound

GeoAMV

METAR

SSMIS_RV

Synop

Observations Assimilated

* Not operationally assimilated by AFWA at the time the study was conducted


Special Sensor Microwave Imager/Sounder


FSO Configuration: SSMIS Brightness Temperatures Data Selection and Source• Data obtained from FNMOC via NRL Monterey

in BUFR • Data processed through NRL developed

Universal Pre-Processor (UPP) by FNMOC• Resource limitations prevented assimilation of

data from more than one satellite at a time• F17 chosen to be assimilated because of

superior domain coverage for 00 and 12Z cycles


Fleet Numerical Meteorology and Oceanography Center (FNMOC)


Binary Universal Form for the Representation of meteorological data (BUFR)


DMSP Flight 17 (F17)


FSO Configuration: SSMIS Brightness Temperatures Quality Control• Data thinned to reduced the effect of correlated

observation error• Channels whose weighting functions peaked

close to surface or above model top not used• Data near coastlines not used• Extensive QC….8 separate tests

– Data over mixed sfc type not used– Data over precip areas not used– Data over heavy cloud cover not used– Two ob minus background gross checks

• Suspected bad channels “blacklisted”


FSO Configuration: SSMIS Brightness Temperatures Variational Bias Correction


FSO Configuration: SSMIS Brightness Temperatures Variational Bias Correction


Observation minus background with bias correction applied (OMB_wb)Observation minus background no bias correction applied (OMB_nb)


Forecast Error Sensitivities at the Initial and Forecast Times

(F/ x0 )U (F/ xf )U

Initial Time: 28 Jan 2012 at 12 Z Forecast Time: 29 Jan 2012 at 12 Z

(oK)(oK)

(m s-1) (m s-1)


U is the east-west component of the wind


Theta is Potential Temperature


Single Day Impact Results: 28 Jan 2012 at 12 Z

• Number of obs assimilated indicated at the end of the bar

• Feature track winds (GeoAMV) largest number of observations and largest impact

• SSMIS TPW and OSWS retrievals (SSMIS_RV) have a large impact

• Surface observations (Synop and METAR) have large impact

Larg

er Im

pact

Observation Impact on 24-Hour Forecast (Base Time: 2012012812) Sound Synop GeoAMV AIREP GPSRF METAR Ships SSMIS_RV Buoy SSMIS

Sound Synop GeoAMV AIREP GPSRF METAR Ships SSMIS_RV Buoy SSMIS

% o

f Err

or R

educ

tion

Attr

ibut

able

to G

iven

Obs

erva

tion

Type

Observation Type:Sound radiosondeSynop surface weather reports

GeoAMV winds from tracking features from Geo MetSats

AIREP aircraft reportsGPSRF GPS refractivitiesMETAR surface weather reportsShips surface reports from ships

SSMIS_RV SSMIS TPW and OSWS

Buoy surface reports from buoys

SSMIS brightness temperatures from temperature and humidity channels


Individual Observation Impact, 28 Jan 2012 12Z

• Radiosondes– Wind, Temps,

Humidity– Most

observations are outside of area of interest but still reduce the forecast error

– Humidity obs (Q) positively impact “dry” energy forecast

V

T Q

U

green to blue shaded dot indicates forecast error reduction

yellow to red shaded dot indicates forecast error increase


U is the east-west component of the wind


V is the north-south component of the wind


T is Temperature


Q is specific humidity


T

Observation Impact, 28 Jan 2012 12Z

• Aircraft Reports (AIREPs)– Multiple levels– Wind and temperature – Automated and manual– Some tracks show

pattern of positive to negative impact over the track (valid time of ob?)

U

V





• Feature Track Winds (GeoAMVs)– Multiple levels– Number of

observations varies greatly day to day V

U




P (all)


• METARs– Wind, Temps,

Pressure, Humidity

– Nearly 50% of observations are not used mostly in complex terrain

– Humidity obs (Q) positively impact “dry” energy forecast

P (used)

T(all) T(used) U(all) U(used)

V(all)V(used)

Q(all) Q(all)

black dot indicates the ob not used




P is pressure


Observation Impact, 28 Jan 2012 12Z• GPS Refractivities

(GPSRF)– Refractivity– Multiple levels– Generally only 1- 3

observations in the domain

– Generally the highest impact per ob

• Ship– Mostly temp &

winds – A few press &

humidity– Can include buoys

• Buoy– Same as ships – From buoy data

center

GPSRF Ship T

BuoyT




Monthly Average Observation Impact: January 2012

Observation Type:

Sound Radiosonde

Synop surface weather reports

GeoAMV winds from tracking features from Geo MetSats

AIREP aircraft reports

GPSRF GPS refractivities

METAR surface weather reports

Ships surface report from ships

SSMIS_RV SSMIS TPW and wind speed

Buoy surface reports from buoys

SSMISbrightness temperatures from temperature and humidity channels

Larg

er Im

pact

Average Observation Impact on 24-Hour Forecast (1-29 January 2012) Sound Synop GeoAMV AIREP GPSRF METAR Ships SSMIS_RV Buoy SSMIS

% o

f Err

or R

educ

tion

Attr

ibut

able

to G

iven

Obs

erva

tion

Type

Sound Synop GeoAMV AIREP GPSRF METAR Ships SSMIS_RV Buoy SSMIS

Combined affect of SSMI/S (retrievals of ocean surface wind speed, total precipitable water, and brightness temperatures for temperature and humidity channels) is nearly 15% of the total error reduction achieved through the assimilation of all observations


Summary

• The observation impact assessment tool Forecast Sensitivity to Observations was used to determine the relative impact of various types of weather observations on 24-hour WRF model forecast accuracy over portions of South West Asia

• The system was modified so SSMIS brightness temperatures were assimilated and their impact assessed

• On average, the assimilation of SSMIS brightness temperatures, total precipitable water, and ocean surface wind speeds accounted for nearly 15% of the error reduction achieved through the assimilation of all observations


Total Precipitable Water (TPW) -- an integrated measure of the amount of water vapor in the atmospheric column


Future WorkGridpoint Statistical Interpolation (GSI) System

• AFWA recently transitioned from WRF DA to GSI

• AFWA had NCAR modify FSO to work with the GSI

• Validation efforts underway

• New capabilities– observation impact

by level and by channel

– Capacity to determine impact for multiple satellite sensors

• Additional Domains

Observation Impact on Mesoscale Model Forecast Accuracy over Southwest Asia

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