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Forecast Sensitivity Observation Impact (FSOI). • in This Presentation, Recent Research Results on FSOI in East Asia and Arctic Are Presented

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Forecast Sensitivity Observation Impact (FSOI). • in This Presentation, Recent Research Results on FSOI in East Asia and Arctic Are Presented Forecast Sensitivity Observation Impact in East Asia and Arctic Hyun Mee Kim1, Dae-Hui Kim1, and Sung-Min Kim2 1Yonsei University, 2Korea Meteorological Institute 1 December 2020 7th Workshop on the Impact of Various Observing Systems on NWP Overview Background Forecast sensitivity observation impact (FSOI) in East Asia Forecast sensitivity observation impact (FSOI) in Arctic Implications Background • Recently, the number of observations used in a data assimilation system is increasing enormously. Because it is not clear whether all these observations are always beneficial to the performance of the NWP, it is important to evaluate the impact of particular observations on the forecast quantitatively to provide relevant information about the impact of the observing system. • Traditionally, the impact of observations has been assessed with observation system experiments (OSEs). The OSEs require much computational resources. • The alternative way to evaluate the impact of observations on the forecast is the Forecast Sensitivity Observation Impact (FSOI). • In this presentation, recent research results on FSOI in East Asia and Arctic are presented. Effect of enhanced satellite-derived atmospheric motion vectors on numerical weather prediction in East Asia (Kim et al. 2017) • The WRF model with 3DVAR and its adjoint are used to evaluate the impact of several types of observations, including enhanced satellite-derived atmospheric motion vectors (AMVs) that were made available during observation campaigns for two typhoons: Sinlaku and Jangmi, which both formed in the western North Pacific during September 2008. Experimental setting and domain • Model : WRFv3.3 and 3DVAR DA (27 km resolution) • Period : 25 August 2008 ~ 30 September 2008 • Conventional observations (±3 hour window) : SYNOP, SHIPS, BUOY, METAR, SOUND, PILOT, PROFILER, GPSPW, QSCAT, AMSUA • Enhanced AMVs (±1.5 hour window) : from the MTSAT by CIMSS Conventional observations Enhanced AMVs Experiment Others AMV 1h AMV EXP0 O X X EXP1 O O X EXP2 O O O FSOI • The observation impact of the enhanced AMVs is large compared to the impact of conventional AMVs, but smaller than SOUND and AMSU-A. • For all experiments, TB shows the largest observation impact followed by U and V. • By assimilating the enhanced AMVs, observation impacts of U and V increase. • When the enhanced AMVs are assimilated, the observation impact of AMSU-A decreases on average, especially for channel-7 which are mainly weighted in the upper troposphere. Typhoon forecast • Both the analysis and 24 h forecast distance error decrease significantly, when the enhanced AMVs are assimilated. • During the period of the TCs, the 24 h forecast error for U and V is reduced by 4.9% when the enhanced AMVs are assimilated. Enhanced AMV effect • Without the assimilation of enhanced AMV data, radiosonde observations and satellite radiances show the highest total observation impact on forecasts. • When enhanced AMVs are included in the assimilation, the observation impact of AMVs is increased and the impact of radiances is decreased. • Enhanced AMVs improve forecast fields when tracking typhoon centers for Sinlaku and Jangmi. Both the model background and the analysis are improved by the continuous cycling of enhanced AMVs, with a greater reduction in forecast error along the background- trajectory than the analysis-trajectory. Effect of assimilating Himawari-8 atmospheric motion vectors on forecast errors over East Asia (Kim and Kim 2018) Experiment name geoAMV used for assimilation Exp1 MTSAT-2 AMVs (QI ≥ 70) • The energy-norm forecast Exp2 HIMA-8 AMVs (QI ≥ 70) error was reduced more by Exp3 HIMA-8 AMVs (QI ≥ 94) replacing MTSAT-2 AMVs MTSAT-2 AMVs (QI ≥ 70) Exp4 + HIMA-8 AMVs (QI ≥ 70) with HIMA-8 AMVs than by MTSAT-2 AMVs (QI ≥ 70) Exp5 adding HIMA-8 AMVs to the + HIMA-8 AMVs (QI ≥ 94) MTSAT-2 AMVs. • When the HIMA-8 AMVs replaced or added to MTSAT-2 AMVs, the observation impact was reduced, which implies the analysis-forecast system was improved by assimilating HIMA-8 AMVs. Forecast sensitivity observation impact in the 4DVAR and Hybrid-4DVAR data assimilation systems (Kim and Kim 2019) ] -1 0.4 -1 -1 Hybrid-4DVAR: -12.42 J kg day AMSU-A BUOY 4DVAR: -11.07 J kg-1 day-1 0.0 IASI MSG TEMP MFG AIRCRAFT -0.4 MHS SYNOP ASCAT GOES GOES -0.8 MSG HIRS -1.6 AIRS MTSAT BUOY IASI -2.4 METAR AIRCRAFT ASCAT SHIP -3.2 MHS TEMP -4.0 MTSAT METAR MFG SYNOP Forecast error reduction[J kg Forecast error -4.8 GPSRO PILOT 2014-8-5 2014-8-10 2014-8-15 2014-8-20 2014-8-25 HIRS COMSCSR SHIP COMSAMV Date PILOT AMSU-A COMSAMV AIRS PRFL PRFL Dropsonde Dropsonde Hybrid-4DVAR • The observation impact was largest in COMSCSR GPSRO TCBOGUS 4DVAR a TCBOGUS b AMSU-A followed by IASI, TEMP, -1.6 -1.2 -0.8 -0.4 0.0 0.4 30 35 40 45 50 55 60 -1 -1 AIRCRAFT, and SYNOP. Total impact [J kg day ] Fraction of beneficial observation [%] • The beneficial observation rate is AMSU-A HIRS IASI MHS AIRS COMSCSR approximately 50%. MHS AMSU-A GPSRO IASI HIRS AIRS • In Hybrid-4DVAR, the observation COMSCSR GPSRO GOES COMSAMV MSG MTSAT impacts for all observation types increase ASCAT GOES MFG ASCAT MTSAT MFG except for Dropsonde, PILOT, and wind COMSAMV MSG AIRCRAFT Dropsonde profiler (PRFL), compared to those in TEMP AIRCRAFT PILOT PILOT Dropsonde TEMP 4DVAR. PRFL PRFL SYNOP TCBOGUS BUOY Satellite sounding BUOY • The increase of the beneficial METAR Satellite wind METAR SHIP Ground sounding SYNOP observations in Hybrid-4DVAR is due to TCBOGUS Surface c SHIP d -0.20 -0.15 -0.10 -0.05 0.00 0.05 -2 0 2 4 10 12 14 the smaller analysis error in Hybrid- Additional impact [J kg-1 day-1] Additional fraction [%] 4DVAR compared to the 4DVAR. Forecast sensitivity observation impact in the 4DVAR and Hybrid-4DVAR data assimilation systems (Kim and Kim 2019) 06 and 18 UTC analyses 00 and 12 UTC analyses AMSU-A IASI AIRS MHS GPSRO HIRS COMSCSR GOES MSG ASCAT MFG MTSAT COMSAMV AIRCRAFT TEMP PILOT Dropsonde PRFL SYNOP BUOY Satellite sounding METAR Satellite wind SHIP Ground sounding TCBOGUS Surface a b -0.15 -0.10 -0.05 0.00 0.05 -0.15 -0.10 -0.05 0.00 0.05 Additional impact [J kg-1 day-1] Additional impact [J kg-1 day-1] • The observation impact of AMVs in East Asia is sensitive to the integration time of the ensemble members used for deducing the flow-dependent BEC in Hybrid-4DVAR. Forecast sensitivity observation impact in Arctic • Investigate the adjoint-based FSOI over the Arctic. • Model : PWRF v3.8.1 and 3DVAR (30 km resolution) • Period : 26 July 2018 ~ 31 August 2018 • Conventional observations (±3 hour window) : SYNOP, SHIPS, BUOY, METAR, SOUND, PILOT, PROFILER, GPSPW, QSCAT, AMSUA FER and FSOI Latitudinal distribution of FSOI Implications • Although generally similar, detailed FSOI results vary depending on the region, model, and DA system used. • Beneficial observation rates increase in the regional modeling system compared to the global modeling system. • The normalized observation impacts for SOUND and AMV near 90 N are considerably large compared to those in other latitudes, indicating the importance of the observing system in Arctic. Thank you.
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