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TRMM Latent Heating Retrieval: Applications and Comparisons with Field Campaigns and Large-Scale Analyses
68
Citations
124
References
2016
Year
EngineeringMachine LearningLarge-scale AnalysesWeather ForecastingClimate ModelingField CampaignsEarth ScienceLatent HeatingLatent ModelingNumerical Weather PredictionData ScienceAtmospheric ScienceMeteorological MeasurementHydroclimate ModelingAtmospheric ModelingStatisticsLatent Variable MethodsHydrometeorologyMeteorologyPredictive AnalyticsGeographyRadiation MeasurementLatent Variable ModelAbstract YanaiFunctional Data AnalysisClimate DynamicsClimatologyRemote SensingSatellite MeteorologyLh ProfilesClimate Modelling
Abstract Yanai and coauthors utilized the meteorological data collected from a sounding network to present a pioneering work in 1973 on thermodynamic budgets, which are referred to as the apparent heat source (Q1) and apparent moisture sink (Q2). Latent heating (LH) is one of the most dominant terms in Q1. Yanai’s paper motivated the development of satellite-based LH algorithms and provided a theoretical background for imposing large-scale advective forcing into cloud-resolving models (CRMs). These CRM-simulated LH and Q1 data have been used to generate the look-up tables in Tropical Rainfall Measuring Mission (TRMM) LH algorithms. A set of algorithms developed for retrieving LH profiles from TRMM-based rainfall profiles is described and evaluated, including details concerning their intrinsic space–time resolutions. Included in the paper are results from a variety of validation analyses that define the uncertainty of the LH profile estimates. Also, examples of how TRMM-retrieved LH profiles have been used to understand the life cycle of the MJO and improve the predictions of global weather and climate models as well as comparisons with large-scale analyses are provided. Areas for further improvement of the TRMM products are discussed.
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