Impact of hyperspectral IR radiances on wind analyses

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Presentation transcript:

Impact of hyperspectral IR radiances on wind analyses Kirsti Salonen and Anthony McNally Kirsti.Salonen@ecmwf.int

European Centre for Medium-Range Weather Forecasts Motivation The upcoming hyper-spectral IR instruments on geostationary satellites will provide information with high vertical and temporal resolution. Positive impact on wind analysis/forecasts has been demonstrated with Geostationary radiances (Peuby and McNally 2009, Lupu and McNally 2012, Lupu and McNally 2013) Microwave instruments in the all-sky framework (Geer et al, 2014). Here focus is on the current hyper-spectral IR instruments on board polar orbiting satellites. European Centre for Medium-Range Weather Forecasts

Radiance observations in 4D-Var, impact on wind analysis Adjustments in the mass fields of the atmosphere. Assimilation system has freedom to adjust the wind field of the initial conditions directly. xa = xb + K (y - Hxb) 220 270 -5 5 K = BHT(HBHT+R)-1 Example: IASI channel 3002

Example: 300 hPa R and wind analysis increments 1.11.2016 00 UTC WV absorption band channels from IASI, CrIS and AIRS used in assimilation No WV absorption band channels from hyperspectral IR instruments used in assimilation -50% 0 50% -50% 0 50%

Experimentation setup IFS cycle 43R3, 1.11-31.12.2016 Baseline: Conventional observations + AMSU-A HyIR: Baseline + IASI (Metop-A, Metop-B), Cris, AIRS AMV: Baseline + all operationally used AMVs All: Full observing system 12-hour sample coverage of active hyperspectral IR data Metop-A IASI Metop-B IASI Aqua AIRS Suomi-NPP Cris European Centre for Medium-Range Weather Forecasts

Differences in the mean wind analysis (HyIR-Baseline, 1.11-31.12.2016) 850 hPa 300 hPa -2 -1 0 1 2 -2 -1 0 1 2 European Centre for Medium-Range Weather Forecasts

European Centre for Medium-Range Weather Forecasts Wind analysis scores Wind analysis error: departure from the ECMWF analysis using full observing system. The analysis error is compared to that of Baseline experiment. Wind analysis score = 0%, no improvement over the baseline experiment (conventional + AMSU-A) Wind analysis score = 100%, no error with respect to the full observing system analysis European Centre for Medium-Range Weather Forecasts

European Centre for Medium-Range Weather Forecasts Wind analysis scores NH TR SH Pressure (hPa) Pressure (hPa) Pressure (hPa) Wind analysis score (%) Wind analysis score (%) Wind analysis score (%) European Centre for Medium-Range Weather Forecasts

Impact on short range wind forecasts HyIR: CTL + 2 IASI + CrIS + AIRS AMV: CTL + All operatinally used AMVs Good Bad Wind, NH Wind, TR Wind, SH Pressure (hPa) Pressure (hPa) Pressure (hPa) FG sdev (%, normalised) FG sdev (%, normalised) FG sdev (%, normalised) European Centre for Medium-Range Weather Forecasts

European Centre for Medium-Range Weather Forecasts Role of spatial and temporal sampling CTL: Conv + AMSU-A EXP: CTL + Metop-A IASI Wind Pressure (hPa) FG sdev (%, normalised) Metop-A Verification over Meteosat-11 disc European Centre for Medium-Range Weather Forecasts

European Centre for Medium-Range Weather Forecasts Role of spatial and temporal sampling CTL: Conv + AMSU-A EXP: CTL + 2 IASI Wind Pressure (hPa) FG sdev (%, normalised) Metop-A Metop-B Verification over Meteosat-11 disc European Centre for Medium-Range Weather Forecasts

European Centre for Medium-Range Weather Forecasts Role of spatial and temporal sampling CTL: Conv + AMSU-A EXP: CTL + 2 IASI + CrIS Wind Pressure (hPa) FG sdev (%, normalised) Metop-A Metop-B CrIS Verification over Meteosat-11 disc European Centre for Medium-Range Weather Forecasts

European Centre for Medium-Range Weather Forecasts Role of spatial and temporal sampling CTL: Conv + AMSU-A EXP: CTL + 2 IASI + CrIS + AIRS Wind Pressure (hPa) FG sdev (%, normalised) Metop-A Metop-B CrIS AIRS Verification over Meteosat-11 disc European Centre for Medium-Range Weather Forecasts

European Centre for Medium-Range Weather Forecasts Conclusions Assimilation of humidity sensitive radiance observations in 4D-Var impact the wind analysis via Adjustments in the mass fields of the atmosphere. Adjustments in the wind field directly Hyperspectral IR observations from polar orbiting satellites have clear positive impact on wind analysis and forecasts. Majority of the impact comes from the water vapour channels Currently only 10 water vapour channels from IASI and 7 channels both from CrIS and AIRS are used operationally Upcoming hyperspectral IR instruments on geostationary satellites will provide observations up to 30 min time resolution and have enormous potential for NWP. European Centre for Medium-Range Weather Forecasts