PRECIPITATION-RELATED RADIANCE BIAS CORRECTION HOW GPM CAN HELP AND ¡ Sara Zhang 1 , Philippe Chambon 2 , William Olson 1 , Milija Zupanski 3 and Arthur Hou 1 1 NASA ¡GODDARD ¡SPACE ¡FLIGHT ¡CENTER ¡ 2 CNRM-‑GAME, ¡MÉTÉO-‑FRANCE ¡AND ¡CNRS ¡ 3 CIRA, ¡COLORADO ¡STATE ¡UNIVERSITY ¡ ¡ THE 6 TH WMO SYMPOSIUM ON DATA ASSIMILATION
WHAT is GPM? Glo lobal l Precipitation n Measureme ment nt a NASA & JAXA joint satellite mission to be launched in February 2014 • New generation satellite observations • Extensive ground validation data collection • Advance of radiative transfer modeling with precipitation
RADIANCE BIAS AFFECTED BY PRECIPITATION In satellite data assimilation, bias between observed and model-simulated radiances represents a combination of instrument measurement bias, systematic errors in observation operators, and forecast model errors projected in observation space. Precipitation-sensitive microwave radiances are particularly susceptible to approximations and assumptions on physical properties of precipitation in radiative transfer calculations and model cloud physics schemes. Forecast model errors ! hydrometeor phase and amount predicted by model microphysics, storm displacement Radiative transfer model errors hydrometeor shape and size distribution, optical property approximation Measurement bias orbital condition, calibration
EMPIRICAL BIAS CORRECTION: USING SCATTERING INDEX OVERLAND (SIL) OF RADIANCES AS A PREDICTOR Using multi-channel MW radiances, FG-departure statistics are based on symmetrical sampling categorized by the strength of scattering signals both in observations and first guess. Bias model using averaged SIL OBS FG predictor O-F -F
EMPIRICAL BIAS CORRECTION: USING SCATTERING INDEX OVERLAND (SIL) OF RADIANCES AS A PREDICTOR Using multi-channel MW radiances, FG-departure statistics are based on symmetrical sampling categorized by the strength of scattering signals both in observations and first guess. Bias model using averaged SIL OBS FG predictor O-F -F How about a physically-derived radiance bias estimation, particularly related to hydrometeor size distribution and phase?
GPM CORE OBSERVATORY : DPR and GMI Dual-frequency Precipitation Radar GPM-emulated Ku and Ka 13.6 GHz (Ku) , 35.5 GHz (Ka) from NASA Aircraft-borne APR-2 field campaign GPM Microwave Imager 10.7, 18.7, 23.8, 36.5, 89.0, 165.5, 183±8, , 183±3 GHz
RADAR DUAL FREQUENCY RATIO (DFR) and HYDROMETEOR PHYSICAL PROPERTIES (PSD) Radar measurements in reflectivity Hydrometeor size distribution λ 4 { } N ( D ) = N 0 D µ exp −Λ D ∞ ∫ z e = N ( D ) σ b ( D , λ , T ) dD π 5 K w 2 0
RADAR DUAL FREQUENCY RATIO (DFR) and HYDROMETEOR PHYSICAL PROPERTIES (PSD) Radar measurements in reflectivity Hydrometeor size distribution λ 4 { } N ( D ) = N 0 D µ exp −Λ D ∞ ∫ z e = N ( D ) σ b ( D , λ , T ) dD π 5 K w 2 0 concentration frequency variability of size back-scattering cross section proportion of large/small size hydrometeor size distribution
RADAR DUAL FREQUENCY RATIO (DFR) and HYDROMETEOR PHYSICAL PROPERTIES (PSD) Radar measurements in reflectivity Hydrometeor size distribution λ 4 { } N ( D ) = N 0 D µ exp −Λ D ∞ ∫ z e = N ( D ) σ b ( D , λ , T ) dD π 5 K w 2 0 concentration frequency variability of size back-scattering cross section proportion of large/small size hydrometeor size distribution Dual-frequency ratio Ice-phase hydrometeor density DFR = Z ku ρ s = α D − β Z ka
RADAR DUAL FREQUENCY RATIO (DFR) and HYDROMETEOR PHYSICAL PROPERTIES (PSD) Radar measurements in reflectivity Hydrometeor size distribution λ 4 { } N ( D ) = N 0 D µ exp −Λ D ∞ ∫ z e = N ( D ) σ b ( D , λ , T ) dD π 5 K w 2 0 concentration frequency variability of size back-scattering cross section proportion of large/small size hydrometeor size distribution Dual-frequency ratio Ice-phase hydrometeor density DFR = Z ku ρ s = α D − β Z ka DFR is independent of N o, ρ is inversely proportional to D and a good proxy for mean mass diameter D m indicated by observations
RADAR DUAL FREQUENCY RATIO (DFR) and HYDROMETEOR PHYSICAL PROPERTIES (PSD) Radar measurements in reflectivity Hydrometeor size distribution λ 4 { } N ( D ) = N 0 D µ exp −Λ D ∞ ∫ z e = N ( D ) σ b ( D , λ , T ) dD π 5 K w 2 0 concentration frequency variability of size back-scattering cross section proportion of large/small size hydrometeor size distribution Dual-frequency ratio Ice-phase hydrometeor density DFR = Z ku ρ s = α D − β Z ka DFR is independent of N o, ρ is inversely proportional to D and a good proxy for mean mass diameter D m indicated by observations Use DFR to infer PSD parameters assumed in the radiative transfer model Use radar-data-adjusted parameters to correct bias in FG MW radiances
WHAT OBSERVATIONS SAY ABOUT DFR and PSD PARAMETERS (from in-situ field campaign data) DFR and Ku can identify Snow density and diameter hydrometeor phases are inversely related Liao, L. and R. Meneghini, 2011: A Study on the Feasibility of Dual-Wavelength Radar for Identification of Hydrometeor Phases. J. Appl. Meteor. Climatol. , 50 50, 449–456.
PHYSICALLY-DERIVED BIAS CORRECTION USING DFR: an IDEALIZED OBSERVATION EXPERIMENT Radar & MW observations and FG are simulated with different PSD parameters. FG-departures reflect only this bias in radiance observation operator. DFR (OBS) 89GHz (OBS)
PHYSICALLY-DERIVED BIAS CORRECTION USING DFR: an IDEALIZED OBSERVATION EXPERIMENT Radar & MW observations and FG are simulated with different PSD parameters. FG-departures reflect only this bias in radiance observation operator. DFR (OBS) Ice-phase particle scattering BT (K) FG 89GHz (FG) bias 89GHz (OBS) BT (K) OBS
PHYSICALLY-DERIVED BIAS CORRECTION USING DFR: an IDEALIZED OBSERVATION EXPERIMENT Radar & MW observations and FG are simulated with different PSD parameters. FG-departures reflect only this bias in radiance observation operator. DFR of Ka and Ku radar observations are used to infer PSD parameters (D m and ρ ) FG MW radiances (89GHz) are recalculated using radar-inferred PSD to reduce bias DFR DFR (OBS) Ice-phase particle OB OBS S ESTM ES M scattering FG G BT (K) FG 89GHz (FG) bias 89GHz (OBS) 89GHz OB OBS S FG_B G_BC BT (K) OBS FG G
CONSTRUCTING OSSE FOR GMI & DPR Simulations by Météo-France cloud-scale model (AROME) are used to create synthetic GPM observations. The Goddard cloud-scale ensemble data assimilation system uses WRF with Goddard cloud physics. FG-departures mimic realistic distribution of precipitation-related errors. OBS (AROME) FG (WRF) Surface rain Ku Ku FG-Departure 89GHz DFR DFR
COLLECTING STATISTICS IN OSSE FOR GMI & DPR FG-departure 89GHz Averaged DFR with SIL symmetrical sampling Sample counts
Implementation Strategy for Goddard cloud-scale EnDAS Ensemble filter analysis Background DPR to produce increments on model state Hydrometeors, Z ku , Z ka (mixing ratio of hydrometeors, etc.) T, q etc. Radar reflectivity Simulation, DFR GMI radiance Radiance Simulation With DPR-derived bias estimation of PSD Background correction parameters Hydrometers, T, q etc. Eventually to adaptive bias correction parameter augmentation and simultaneous estimation in ensemble filter
SUMMA MMARY Biases in precipitation-sensitive radiances are related to approximations and assumptions on hydrometeor PSD in radiative transfer calculations and model cloud physics schemes. GPM dual-frequency precipitation radar data can be used to infer PSD parameters in a physically-derived bias correction scheme for precipitation-affected radiances. Development and implementation are ongoing in OSSE using synthetic GPM observations and in real data assimilation experiments using NASA field campaign observations. THIS WORK IS A COLLABORATION OF NASA GPM SCIENCE PROGRAM AND MÉTÉO-FRANCE
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