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

Reale, Oreste; Lau, K.-M.; Susskind, Joel; Rosenberg, R.

doi:10.1029/2011jd017093

Cited by 20 publications

(9 citation statements)

References 33 publications

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“…For example, weather forecast offices utilize EDR via the Advanced Weather Interactive Processing System, and EDR data sets are used for numerous environmental research studies [e.g., Pagano, 2013]. Furthermore, EDR retrievals based upon cloud-cleared radiances (CCRs) [e.g., Susskind et al, 2003;Maddy et al, 2011] have been shown to have a positive impact on forecast models [e.g., Le Marshall et al, 2008;Reale et al, 2008bReale et al, , 2009Reale et al, , 2012aZhou et al, 2010].…”

Section: "Partly Cloudy" (Ir + Mw)mentioning

confidence: 99%

Validation of satellite sounder environmental data records: Application to the Cross‐track Infrared Microwave Sounder Suite

Nalli

Barnet²,

Reale

et al. 2013

JGR Atmospheres

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The Joint Polar Satellite System (JPSS) Cross‐track Infrared Microwave Sounder Suite (CrIMSS) is an advanced operational satellite sounding system concept comprised of the Cross‐track Infrared Sounder and the Advanced Technology Microwave Sounder. These are synergistically designed to retrieve key environmental data records (EDR), namely atmospheric vertical temperature, moisture, and pressure profiles. CrIMSS will serve as the low‐Earth orbit satellite sounding system, starting with the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite and spanning the JPSS‐1 and JPSS‐2 satellites. This paper organizes the general paradigm for validation of atmospheric profile EDR retrieved from satellite nadir sounder systems (e.g., CrIMSS) as a synthesis of complementary methods and statistical assessment metrics. The validation methodology is ordered hierarchically to include global numerical model comparisons, satellite EDR intercomparisons, radiosonde matchup assessments (conventional, dedicated, and reference), and intensive campaign “dissections.” We develop and recommend the proper approach for computing profile statistics relative to correlative data derived from high‐resolution in situ data (viz., radiosondes) reduced to forward model layers. The standard statistical metrics used for EDR product assessments on “coarse layers” are defined along with an overview of water vapor weighting schemes and the use of averaging kernels. We then overview the application of the methodology to CrIMSS within the context of the JPSS calibration/validation program, with focus given to summarizing the core data sets to be used for validation of S‐NPP sounder EDR products.

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Section: "Partly Cloudy" (Ir + Mw)mentioning

confidence: 99%

Validation of satellite sounder environmental data records: Application to the Cross‐track Infrared Microwave Sounder Suite

Nalli

Barnet²,

Reale

et al. 2013

JGR Atmospheres

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“…We chose the period to cover the set of catastrophic rain episodes that occurred in late July 2010 along the Indus Valley, in central and northern Pakistan. Reale et al 8 showed 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 than assimilation of radiances unaffected by clouds.…”

Section: Forecast Impact Experiments Using Quality Controlled Versionmentioning

confidence: 99%

“…We started conducting preliminary experiments at GSFC, assimilating AIRS Version-5 quality controlled temperature profiles into the NCEP operational GDAS system and comparing the results to those using the operational procedure of assimilating AIRS radiances, and also to a Control run which assimilated no AIRS data. These preliminary experiments were conducted starting from July 15, 2010, just as those studied by Reale et al 8 .…”

Section: Forecast Impact Experiments Using Quality Controlled Versionmentioning

confidence: 99%

Recent advances in improvement of forecast skill and understanding climate processes using AIRS Version-5 products

Susskind

Molnar

Iredell³

et al. 2012

SPIE Proceedings

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The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) generates products derived from AIRS/AMSU-A observations, starting from September 2002 when the AIRS instrument became stable, using the AIRS Science Team Version-5 retrieval algorithm. This paper shows results of some of our research using Version-5 products from the points of view of improving forecast skill as well as aiding in the understanding of climate processes.

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“…Later, many case studies have attempted to improve the monsoon rainfall in NWP models using various data assimilation methods [e.g., Govindankutty et al, 2008;Rakesh et al, 2009Rakesh et al, , 2010Routray et al, 2010;Singh et al, 2012]. Reale et al [2012] showed improvement in the global forecast skill by assimilating AIRS temperature profiles. They also achieved considerable improvement in analysis as well as forecast of precipitation up to 7 days.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the impact of AIRS profiles on prediction of Indian summer monsoon using WRF variational data assimilation system

et al. 2015

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This study investigates the impact of temperature and moisture profiles from Atmospheric Infrared Sounder (AIRS) on the prediction of the Indian summer monsoon, using the variational data assimilation system annexed to the Weather Research and Forecasting model. In this study, three numerical experiments are carried out. The first is the control and includes no assimilation; in the second, named Conv, assimilation of conventional Global Telecommunication System data is performed. The third one, named ConvAIRS, is identical to the Conv except that it also includes assimilation of AIRS profiles. The initial fields of tropospheric temperature and water vapor mixing ratio showed significant improvement over the model domain. Assimilation of AIRS profiles has significant impact on predicting the seasonal mean monsoon characteristics such as tropospheric temperature, low-level moisture distribution, easterly wind shear, and precipitation. The vertical structure of the root-mean-square error is substantially affected by the assimilation of AIRS profiles, with smaller errors in temperature, humidity, and wind magnitude. The consequent improved representation of moisture convergence in the boundary layer (deep convection as well) causes an increase in precipitation forecast skill. The fact that the monsoonal circulation is better captured, thanks to an improved representation of thermal gradients, which in turn leads to more realistic moisture transport, is particularly noteworthy. Several previous data impact studies with AIRS and other sensors have focused on the short or medium range of the forecast. The demonstrated improvement in all the predicted fields associated with the Indian summer monsoon, consequent to the month long assimilation of AIRS profiles, is an innovative finding with large implications to the operational seasonal forecasting capabilities over the Indian subcontinent.

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

Cited by 20 publications

References 33 publications

Validation of satellite sounder environmental data records: Application to the Cross‐track Infrared Microwave Sounder Suite

Validation of satellite sounder environmental data records: Application to the Cross‐track Infrared Microwave Sounder Suite

Recent advances in improvement of forecast skill and understanding climate processes using AIRS Version-5 products

Evaluation of the impact of AIRS profiles on prediction of Indian summer monsoon using WRF variational data assimilation system

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