WRFDA 2012 Overview and Recent Adjoint-based Observation Impact Studies Hans Huang National Center for Atmospheric Research (NCAR is sponsored by the National Science Foundation) Acknowledge: NCAR/NESL/MMM/DAS, NCAR/RAL/JNT/DAT, AFWA, USWRP, NSF-OPP, NASA, AirDat, PSU, KMA, CWB, CAA, BMB, EUMETSAT

WRFDA Overview Goal: Community WRF DA system for regional/global, research/operations, and deterministic/probabilistic applications. Techniques: 3D-Var 4D-Var (regional) Ensemble DA, Hybrid Variational/Ensemble DA. Model: WRF (ARW, NMM, Global) Observations: Conv. + Sat. + Radar (+Bogus) Support: NCAR/NESL/MMM/DAS (Data Assimilation Section, also supporting WRF/DART) NCAR/RAL/JNT/DAT (Data Assimilation Team, also supporting GSI)

Google WRFDA: www.mmm.ucar.edu/wrf/users/wrfda

Recent Tutorials at NCAR. WRFDA Overview Observation Pre-processing WRFDA System WRFDA Set-up, Run WRFDA Background Error Estimations Radar Data Satellite Data WRF 4D-Var WRF Hybrid Data Assimilation System WRFDA Tools and Verification Observation Sensitivity Practice obsproc wrfda (3D-Var) Single-ob tests Gen_be Radar Radiance 4D-Var Hybrid Advanced (optional) The next: July 2012

WRFDA tutorials 21-22 July, 2008. NCAR. 2-4 Feb, 2009. NCAR. 18 April, 2009. South Korea. 20-22 July, 2009. NCAR. 15-31 Oct, 2009. Nanjing, China. 1-3 Feb, 2010. NCAR. 10 April, 2010. Seoul, South Korea. 3-5 August 2010. NCAR. 16 April. Busan, South Korea 20-22 July 2011. NCAR 10-20 October. Bangkok, Thailand. WRFDA online tutorial and user guide http://www.mmm.ucar.edu/wrf/users/wrfda

New features, v3.3 WRFPLUS3 – WRF TL/AD based on WRF3.3 4D-VAR redesigned/upgraded RTM interface updated: RTTOV (v10.0) CRTM (v2.0.2) NOAA-19 AMSUA and MHS added. New background error option (cv6). Add additional variables, e.g. unbalanced velocity, to balance computations; Fully multivariate including relative humidity Capability to generate forecast sensitivity to observations (FSO), compatible with WRF3.3

A recent adjoint-based observation impact study (or FSO) Thomas Auligné, Xin Zhang, Hongli Wang, Anwei Lai, Xiaoyan Zhang, Hui-Chuan Lin, Qingnong Xiao, Yansong Bao, Zhiquan Liu, Xiang-Yu Huang

FSO - Forecast Sensitivity to Observations Analysis (xa) Forecast (xf) Observation (y) WRF-VAR Data Assimilation WRF-ARW Forecast Model Define Forecast Accuracy Background (xb) Observation Impact <y-H(xb)> (F/ y) Forecast Accuracy (F) Observation Sensitivity (F/ y) Adjoint of WRF-VAR Adjoint of WRF-ARW Derive Forecast Accuracy Background Sensitivity (F/ xb) Analysis Sensitivity (F/ xa) Gradient of F (F/ xf) Obs Error Sensitivity (F/ eob) Bias Correction Sensitivity (F/ k) Figure adapted from Liang Xu (NRL)

xf is the forecast from analysis xa From Langland and Baker (2004) Forecast Error Time xt is the true state, estimated by the analysis at the time of the forecast xf is the forecast from analysis xa xg is the forecast from first-guess at the time of the analysis xa

More details (for WRFDA implementation): Reference state: Namelist ADJ_REF is defined as 1: xt = Own (WRFVar) analysis 2: xt = NCEP (global GSI) analysis 3: xt = Observations Forecast Aspect: depends on reference state 1 and 2: Total Dry Energy 3: WRFDA Observation Cost Function: Jo Geo. projection: Script option for box (default = whole domain) ADJ_ISTART, ADJ_IEND, ADJ_JSTART, ADJ_END, ADJ_KSTART, ADJ_KEND Forecast Accuracy Norm: e = (xf-xt)T C (xf-xt)

Limitations Approximation of “truth” Dependence of norm Linear assumptions Adjoint of the forecast model Adjoint of the analysis (assimilation) …

ASR (180km) Results January 2007

Design of experiments Cycling Data assimilation Start Time:2007081506 Final Time:2007091512 Boundary Conditions : FNL analysis The initial condition for 2007081506 is the FNLdata, The WRF model 6h forecasting is the cycling data assimilation background. ADJ_REF =1, Its own WRFDA analysis ADJ_MEASURE =1

Impact of observations on 24h forecast (conventional observations)

Impact of observations on 24h forecast (satellite observations)

T8 (45km) Results One month:2007081506-2007091512 AWFA T8 WRFV3.2.1 MAP_PROJ=Mercator WE =140 SN =94 DX =45km DY =45km E_VERT =57 MP_PHYSICS=WSM 5-class scheme CU_PHYSICS= Grell 3D ensemble scheme Target region WRFPLUS2.0 ADJ_ISTART=20 ADJ_IEND=60 ADJ_JSTART=20 ADJ_JEND=60 ADJ_KSTART=1 ADJ_KEND=27 WRFV3 Version 3.2.1 WRFDA Version 3.2.1 WRFPLUS2.0

Impact of observations on 6h forecast (grouped as observed variables)

Impact of observations on 6h forecasts (grouped according to observing systems)

SOUND GeoAMV GPSRO hPa hPa Km Vertical Impact distribution, unit: J/Kg

Time series of impact of observations on 6h forecasts SOUND SYNOP GeoAMV X axis :time. 4 times per day during 30days ; Y axis : Impact on the forecasting (Unit:J/kg)

Monitoring the observation impact over Taiwan CWB OP23 domain Xin Zhang and Xiang-Yu Huang

In terms of the observational type The largest error decrease is due to GeoAMV followed by SOUND, SYNOP and GPSREF The impact from SATEM is marginal and neutral. On 0000UTC and 1200UTC, the SOUND is the most important observation to decrease the forecast error, followed by GeoAMV, SYNOP, GPSREF/AIREP on 0600UTC and 1800UTC, the GeoAMV is the most important observation to decrease the forecast error, followed by SYNOP/GPSREF, AIREP

In terms of the time series of impact of observational type On 0000 and 1200UTC, the SOUND improve the forecasts in general. On 0600UTC and 1800UTC, the SOUND almost degrades the 1/3 of the forecasts. Why ? Are the few SOUND obs. bad quality or something else is wrong ? For other observation types, at 0600UTC and 1800UTC, there are many degraded cases due to observations. Need further investigation.

In terms of impact per observation for each type The GPSREF is the most efficient observation to reduce the 24h forecast error per observation, followed by SYNOP, SHIPS and SOUND. It is consistent with the result from AFWA domains ( personal communication with Jason T. Martinelli of AFWA)

SOUND observations around 500hPa (206 records)

500hPa SOUND impact

SYNOP observations

SYNOP impact

In terms of the geographical distribution of observation impact Gives us an insight of the detail information about the observation impact for each observation. Please note: On Taiwan, there are lots SYNOP observations. Due to the plotting technique, the blue dots are overwritten by the red dots.

Preliminary results SOUND is the most important data to improve the forecast among the conventional data. GPSREF is the most efficient data to improve the forecast and is the most important observation at 0600 and 1800UTC. The impact of SATEM is marginal and not stable. For OP23 system, at 0600UTC and 1800UTC, the observation impacts from all observational types are not stable. (Why?) In two days (2010120500 and 2010121812), almost all the observations show the negative impact. Preliminary investigation shows that, in the first case (2010120500), the linear approximation of WRFPLUS is not valid, the nonlinear forecasts show the positive impact of observation, but the WRFPLUS derived the negative impact.

Future research topics Identify observations with continuous negative impact. Tune WRFDA based on observation impact results. Improve the assimilation strategy of surface observation assimilation. Investigate the differences of verifying forecasts to EC analysis, NCEP GFS analysis, WRFDA analysis and Observations.

Future research topics For limited–area forecast models, the forecast errors not only come from initial condition (IC), but also come from lateral boundary condition (LBC). Need to include the initial forecast error gradient with respect to LBC in FSO. Current WRF tangent linear adjoint models (WRFPLUS) only include 3 simplified physics packages. Need to study the accuracy of the linear approximation in FSO applications. We also plan to improve the physics packages in WRFPLUS.

Summary WRFDA 2012 Overview Community System Techniques: 3D-Var, 4D-Var, EnKF, Hybrid New features in v3.3 A recent adjoint-based observation impact study The concept and limitations The WRFDA implementation Applications The next step