Oreste Reale

Impact of assimilated and interactive aerosol on tropical cyclogenesis

This article investigates the impact of Saharan dust on the development of tropical cyclones in the Atlantic. A global data assimilation and forecast system, the NASA GEOS-5, is used to assimilate all satellite and conventional data sets used operationally for numerical weather prediction. In addition, this new GEOS-5 version includes assimilation of aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer. The analysis so obtained comprises atmospheric quantities and a realistic 3-D aerosol and cloud distribution, consistent with the meteorology and validated against Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation and CloudSat data. These improved analyses are used to initialize GEOS-5 forecasts, explicitly accounting for aerosol direct radiative effects and their impact on the atmospheric dynamics. Parallel simulations with/without aerosol radiative effects show that effects of dust on static stability increase with time, becoming highly significant after day 5 and producing an environment less favorable to tropical cyclogenesis.

AIRS impact on analysis and forecast of an extreme rainfall event (Indus River Valley, Pakistan, 2010) with a global data assimilation and forecast system

[1]xa0A set of data assimilation and forecast experiments is performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches toward assimilation of Atmospheric Infrared Sounder (AIRS) data. The impact is first assessed globally on a sample of more than forty forecasts per experiment, through the standard 500xa0hPa anomaly correlation metrics. Next, the focus is on precipitation analysis and precipitation forecast skill relative to one particular event: an extreme rainfall episode which occurred in late July 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that, in addition to improving the global forecast skill, the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7xa0days is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

Improving forecast skill by assimilation of AIRS temperature soundings

This paper uses AIRS temperature profiles derived by the AIRS Science Team Version-5 retrieval algorithm. The AIRS Science Team Version-5 retrieval algorithm is being used operationally at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version-5 retrieval algorithm contains two significant improvements over Version-4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 µm CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 µm CO2 observations are now used primarily in the generation of cloud cleared radiances HRi. This approach allows for the generation of accurate values of HRi and T(p) under most cloud conditions. 2) Another very significant improvement in Version-5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for HRi. These error estimates are used for quality control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of quality control using the NASA GEOS-5 data assimilation system. Assimilation of quality controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecasts resulting from assimilating AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

Improving forecast skill by assimilation of quality controlled AIRS Version 5 temperature soundings

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 μm CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 μm CO2 observations are now used primarily in the generation of cloud cleared radiances Ri. This approach allows for the generation of accurate values of Ri and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for Ri. These error estimates are used for Quality Control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of Quality Control using the NASA GEOS-5 data assimilation system. Assimilation of Quality Controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecast resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

Data Assimilation Experiments Using Quality Controlled AIRS Version 5 Temperature Soundings

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 mum CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 mum CO2 observations are now used primarily in the generation of cloud cleared radiances Rcirci. This approach allows for the generation of accurate values of Rcirci and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for Rcirci. These error estimates are used for quality control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of quality control using the NASA GEOS-5 data assimilation system. Assimilation of quality controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecast resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.