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NASA NTRSDate not supplied

GPCP Version 3.2 Products and Results

The Global Precipitation Climatology Project (GPCP) products address the need for long-term precipitation products that emphasize homogeneity, following Climate Data Record (CDR) principles. The new-generation Version 3.2 provides key improvements over the operational Version 2.3 such as: finer spatial resolution of 0.5°x0.5°; wider geosynchronous infrared estimation (58°N-S) upgraded with the PERSIANN-CDR algorithm; upgraded retrievals from selected passive microwave sensors (GPROF algorithm) that calibrate the IR input; revised intercalibrations of TOVS and AIRS data (used at high latitudes); climatologies based on CloudSat, TRMM, and GPM to provide overall calibration by modern satellite estimates; the latest Global Precipitation Climatology Centre (GPCC) precipitation gauge analyses over land areas; regional modifications to the gauge undercatch correction; and IMERG half-hourly data input to the Daily V3.2 product. We will show sample analyses that demonstrate aspects of the Version 3.2 precipitation record, such as the global climatology, the time series for global land and ocean total precipitation and snowfall, and the time series of tropical land and ocean daily precipitation rate histograms. For selected analyses we will show improvements in both the Monthly and Daily products in Version 3.2 compared to the operational Version 2.3. In particular, the climatological zonal profile of precipitation in the Southern Ocean, extending south of 40°S, improves a suspected artifact in V2.3. Similarly, the Daily histograms over ocean in Version 3.2 lack the jump in the predecessor Version 1.3 Daily over ocean at the start of 2009, although a smaller jump is introduced in June 2014. The presentation will conclude with a prospectus for the future satellites/sensors and community datasets necessary to continue computation of a consistent CDR product on the one hand, while also potentially contributing to improvements in the historical record.

Global Precipitation Measurement↗
NASA NTRS2012

Newly Released TRMM Version 7 Products, Other Precipitation Datasets and Data Services at NASA GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is home of global precipitation product archives, in particular, the Tropical Rainfall Measuring Mission (TRMM) products. TRMM is a joint U.S.-Japan satellite mission to monitor tropical and subtropical (40 S - 40 N) precipitation and to estimate its associated latent heating. The TRMM satellite provides the first detailed and comprehensive dataset on the four dimensional distribution of rainfall and latent heating over vastly undersampled tropical and subtropical oceans and continents. The TRMM satellite was launched on November 27, 1997. TRMM data products are archived at and distributed by GES DISC. The newly released TRMM Version 7 consists of several changes including new parameters, new products, meta data, data structures, etc. For example, hydrometeor profiles in 2A12 now have 28 layers (14 in V6). New parameters have been added to several popular Level-3 products, such as, 3B42, 3B43. Version 2.2 of the Global Precipitation Climatology Project (GPCP) dataset has been added to the TRMM Online Visualization and Analysis System (TOVAS; URL: http://disc2.nascom.nasa.gov/Giovanni/tovas/), allowing online analysis and visualization without downloading data and software. The GPCP dataset extends back to 1979. Version 3 of the Global Precipitation Climatology Centre (GPCC) monitoring product has been updated in TOVAS as well. The product provides global gauge-based monthly rainfall along with number of gauges per grid. The dataset begins in January 1986. To facilitate data and information access and support precipitation research and applications, we have developed a Precipitation Data and Information Services Center (PDISC; URL: http://disc.gsfc.nasa.gov/precipitation). In addition to TRMM, PDISC provides current and past observational precipitation data. Users can access precipitation data archives consisting of both remote sensing and in-situ observations. Users can use these data products to conduct a wide variety of activities, including case studies, model evaluation, uncertainty investigation, etc. To support Earth science applications, PDISC provides users near-real-time precipitation products over the Internet. At PDISC, users can access tools and software. Documentation, FAQ and assistance are also available. Other capabilities include: 1) Mirador (http://mirador.gsfc.nasa.gov/), a simplified interface for searching, browsing, and ordering Earth science data at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Mirador is designed to be fast and easy to learn; 2)TOVAS; 3) NetCDF data download for the GIS community; 4) Data via OPeNDAP (http://disc.sci.gsfc.nasa.gov/services/opendap/). The OPeNDAP provides remote access to individual variables within datasets in a form usable by many tools, such as IDV, McIDAS-V, Panoply, Ferret and GrADS; 5) The Open Geospatial Consortium (OGC) Web Map Service (WMS) (http://disc.sci.gsfc.nasa.gov/services/wxs_ogc.shtml). The WMS is an interface that allows the use of data and enables clients to build customized maps with data coming from a different network.

Liu, Zhong↗
NASA NTRSDate not supplied

The Next-Generation Version 3 GPCP Products

The long-standing Global Precipitation Climatology Project (GPCP) recently introduced its next-generation Version 3.1 Monthly product, , which covers the period 1983-2019. Notable improvements include higher spatial resolution (0.5°x0.5°), geosynchronous IR estimates extended to the latitude band 60°N-S and based on the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) algorithm, and high-latitude (outside 60ºN-S) precipitation estimates based on improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) data. The satellite-only estimate is adjusted to the best available monthly climatologies, namely the Tropical Combined Climatology (TCC) at lower latitudes and the Merged CloudSat, TRMM, and GPM (MCTG) climatology at higher latitudes. Finally, V3.1 merges these climatologically-adjusted satellite-only estimates with the Global Precipitation Climatology Centre (GPCC) gauge analyses at 1°x1°. In addition to the V3.1 Monthly product, the GPCP team is working towards a global Version 3 Daily product based on Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates, where available, calibrated to the GPCP V3.1 Monthly estimate. Rescaled TOVS/AIRS data are used in high-latitude areas that lack IMERG estimates, which occur over snowy/icy surfaces outside 60°N-S. Since IMERG currently extends back to June 2000, daily PERSIANN-CDR data are used for the period January 1983–May 2000 to complete the record. This presentation will provide early results for, and the latest status of, the Monthly and Daily GPCP products as a function of time and region. Key points include examining homogeneity over time and across boundaries between input datasets. One goal is to determine the suitability of the V3 products while we continue to produce the Version 2 GPCP products for on-going use.

precipitation↗
NASA NTRSDate not supplied

High Latitude Considerations in the Latest GPCP monthly and daily products (V3.1)

The Global Precipitation Climatology Project (GPCP) product is a popular combined satellite-gauge precipitation data set in which the long-term standards of consistency and homogeneity is underlined. Here we discuss various high latitude analysis considered in the recently released GPCP V3.1 monthly and daily products. Satellite data are used over land and ocean and obtained from the Special Sensor Microwave Imager (SSMI), Special Sensor Microwave Imager/Sounder (SSMIS), geostationary imagers and polar orbiting infrared sounders. GPCP uses the Global Precipitation Climatology Centre (GPCC) over land, as its in situ component, but prior to combination with satellite data GPCC estimates are adjusted for gauge undercatch. Advanced sensors aboard the Tropical Rainfall Measuring Mission (TRMM), CloudSat, and Global Precipitation Measurement (GPM) mission have enabled more accurate estimation of rain and snowfall rates in recent years. Started with GPCP V3.1 these observations are integrated into GPCP through the development of the Tropical Combined Climatology (TCC) used at lower latitudes and the Merged CloudSat, TRMM, and GPM (MCTG) climatology used over the extra tropics and higher latitudes. Improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation are used outside 60ºN-S, where inside this zone the Goddard Profiling (GPROF) algorithm retrievals from SSMI/SSMIS is used to calibrate geostationary IR based precipitation estimate at monthly scale. The Gravity Recovery and Climate Experiment (GRACE) mass change observations are used to determine snowfall accumulations over frozen land and arctic basins and to assess gauge undercatch corrections. Observations of snow on sea ice from NASA’s Operation IceBridge (OIB) flights are utilized as an additional tool for snowfall assessment over sea ice. GPCP V3.1 has higher spatial resolution (0.5ox0.5o) than earlier versions (2.5ox2.5o) over both land and ocean, going back to 1983. Version 3 Daily product uses the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates, where available (initially restricted to 60°N-S), as well as rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.1 Monthly estimate. GPCP V3.1 shows about 6% increase in global oceanic precipitation and about 4.5% increase over global land and ocean compared to the previous version (V2.3), some major changes occur over the ocean and around 40oS and 60 oS. We will discuss other important changes of GPCP V3.1, compared to the earlier versions, and our future plans. Through this presentation we will also discuss that while ACCP will provide key information about precipitation, synergistic use of other Earth observing systems (e.g., mass change; recognized as a designated mission in 2017 decadal survey) can also help refine precipitation analysis, especially in high latitude and cold regions.

Ali Behrangi↗
NASA NTRS2017

So, How Much of the Earth's Surface Is Covered by Rain Gauges?

The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the de facto standard for precipitation information across Earths surface for hydrometeorological purposes. However, their distribution across the globe is limited: over land their distribution and density is variable, while over oceans very few gauges exist and where measurements are made, they may not adequately reflect the rainfall amounts of the broader area. Critically, the number of gauges available, or appropriate for a particular study, varies greatly across the Earth owing to temporal sampling resolutions, periods of operation, data latency, and data access. Numbers of gauges range from a few thousand available in near real time to about 100,000 for all official gauges, and to possibly hundreds of thousands if all possible gauges are included. Gauges routinely used in the generation of global precipitation products cover an equivalent area of between about 250 and 3,000 sq m. For comparison, the center circle of a soccer pitch or tennis court is about 260 sq m. Although each gauge should represent more than just the gauge orifice, autocorrelation distances of precipitation vary greatly with regime and the integration period. Assuming each Global Precipitation Climatology Centre (GPCC) available gauge is independent and represents a surrounding area of 5-km radius, this represents only about 1% of Earths surface. The situation is further confounded for snowfall, which has a greater measurement uncertainty.

Rain gauge↗
NASA NTRS2012

Projections of Declining Surface-Water Availability for the Southwestern United States

16 of the CMIP5 models had all the data needed for this work for at least one simulation that was continuous from 1950 to 2040. Details of the models analyzed here are provided in Table S1. The model data analyzed here are available at http://strega.ldeo.columbia.edu:81/expert/home/.naomi/.AR5/.v2/.historical:rcp85/.mmm16/ a. Assessing the climatology of the models Despite increases in horizontal resolution of many models compared to their CMIP3 counterparts none of these models can adequately resolve the topography of the south west United States, such as the Sierra Nevada and Rocky Mountains and the associated orographic precipitation. This requires that caution be used when interpreting the results presented here. To assess the ability of the models to simulate the current hydroclimate, in Figure S1 we show the observed (from the Global Precipitation Climatology Centre gridded rain gauge data, (1)) monthly climatology of precipitation and the same for all the models and the multimodel mean for the California-Nevada, Colorado headwaters and Texas regions. The GPCC data uses rain gauges only and interpolates to regular grids of which we used the 1◦ by 1◦ one. Details of the data set can be found in (2). While the models apparently overestimate precipitation in California and Nevada the seasonal cycle with wet winters and dry summers is very well represented. It is also possible that the rain gauge observations are biased low by inadequately sampling the higher mountain regions. How ever the models might also be expected to underestimate orographic precipitation due to inadequate horizontal resolution. The 25 models are also too wet in the Colorado headwaters region but correctly represent the quite even distribution though the year. The bimodal distribution of precipitation in Texas, with peaks in May and September, and the absolute amounts, are well modeled but with the September peak too weak. The positive precipitation bias translates into a positive runoff bias for the Colorado headwaters as also shown in Figure S1. Here the observed runoff values are taken from simulations of the Variable Infiltration Capacity (VIC) land surface-hydrology model (3) forced by observed meteorology (5) that were conducted as part of the North American Land Data Assimilation System project phase 2 ( (NLDAS-2), http://www.emc.ncep.noaa.gov/mmb/nldas/. Runoff for California-Nevada is better simulated but there is a positive bias over Texas despite no strong precipitation bias. To check whether regional climate models better simulate P and runoff in these regions we analyzed the historical simulation with the Regional Climate Model version 3 driven by the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 available from the North American Regional Climate Change Assessment Program (http://www.narccap.ucar.edu). This model configuration retained these biases in P and runoff although they were reduced in amplitude. Given these varying biases we plot P and P − E changes in actual values but apply the simplest bias correction possible to the runoff and soil moisture values and show the modeled changes in terms of percentages of the 20th Century model climatologies. A thorough assessment of the simulation of North American climate in CMIP5 models is conducted in Sheffield at al. (North American Climate in CMIP5 Experiments. Part I: Evaluation of 20th Century Continental and Regional Climatology, manuscript submit ted to J. Climate, available at http://www.climate.noaa.gov/index.jsp?pg=./cpo pa/ mapp/cmip5 publications.html). Sheffield et al. analyze the climatology of precipitation, surface air temperature, low level winds, moisture fluxes, runoff etc. and conclude that the main features of the hydrological cycle, including characteristics of the atmospheric moisture balance and its seasonality, are captured in the CMP5 models subject to biases in total precipitation amounts. We chose to use all available models instead of selecting some and rejecting others based on an assessment of model realism. This is in accord with the suggestions of Mote et al. for CMIP3 (4) but future work needs to revisit this matter for the case of the CMIP5 ensemble.

Seager, Richard↗
NASA NTRS2014

Water Balance in the Amazon Basin from a Land Surface Model Ensemble

Despite recent advances in land surfacemodeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-ofthe- art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 18 spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to matchmonthly Global Precipitation Climatology Project (GPCP) andGlobal Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l'Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets andGravity Recovery and ClimateExperiment (GRACE)TWSestimates in two subcatchments of main tributaries (Madeira and Negro Rivers).At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day(exp -1) and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

GPCP↗
NASA NTRS2011

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch.

Bindlish, Rajat↗
NASA NTRS1999

Multi-Scale Interactions Associated with the Monsoon Onset Over South China Sea and Adjacent Regions during SCSMEX-98

Using data collected during The South China Sea Monsoon Experiment (SCSMEX) (1998) as well as from the TRMM Microwave-Imager (TMI) and precipitation radar (PR), we have studied the multi-scale interactions (meso-synoptic-intraseasonal) associated with monsoon onset over South China Sea (SCS) and its subsequent evolution. Results show that the monsoon onset (defined by development of steady wind direction and heavy precipitation) over the northern SCS occurred around May 15 -17. Prevailing southerlies and southwesterlies developed over the central SCS after May 20. Shortly after, monsoon convection developed over the whole SCS region around May 23-27. The entire onset process appeared to be delayed by about a week to 10 days compared with climatology. During late spring of 1998, mid-latitude frontal systems were particularly active. These systems strongly impacted the northern SCS convection and may have been instrumental in triggering the onset of the SCS monsoon. The Tropical Oceans and Global Atmosphere (TOGA) and Bureau of Meteorology Research Centre (BMRC) radar showed a wide variety of convective systems over the Intensive Flux Array, from frontal bands to shear-banded structure, deep convection, pop-corn type shallow convection, slow moving "fine lines" to water spout. Analysis of SSM/I wind and moisture data suggested that the delayed convective activity over the SCS may be linked to the weakened northward propagation of monsoon rain band, hence contributing to a persistence of the rainband south of the Yangtze River and the disastrous flood that occurred over this region during mid to late June, 1998.

Lau, William K. M.↗
DOE OSTI2026

How Frequent Will the Rarest Daily Rainfall Records of Hurricane Ida’s Remnants Be in the Future?

Abstract Gaining continued insights into the impact of global warming on the occurrence of hurricane-associated intense record downpours is essential for building climate resilient communities. This study investigates projected future changes in extreme rainfall over the Northeast United States, as represented by extreme daily amounts during Hurricane Ida in 2021. We used historical control simulations of Weather Research and Forecasting (WRF) Model generated from 40 years of weather events (1980–2014, 12 km) forced by the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis. These simulations are thermodynamically modified (2060–2100) via an imposed warming for the high-emission scenario of shared socioeconomic pathway (SSP585) from a range of general circulation models. Ground observations from the Global Historical Climatology Network (1950–2014) and WRF simulations (historical, 1980–2014, and future, 2060–2100) are integrated into a nonstationary generalized extreme value (GEV) framework to assess the frequency of Ida’s heaviest daily rain rates under the SSP585 scenario. Results show that Ida’s daily maximum rainfall recorded at different observation locations was higher than the single highest September daily maximum observed (1950–2014) for 5 out of 17 stations (∼30% of the stations). Ida-like extreme daily rain rates are projected to be, on average, more than 2 times more likely to occur at the end of the century in the simulations (with some regions as high as 5 times). This work demonstrates that integrating a high-resolution atmospheric model’s present-day and thermodynamically modified future simulations along with ground observations, within a nonstationary statistical framework, is crucial for understanding changing characteristics of extreme weather events. Significance Statement Daily scale extreme precipitation is expected to become more frequent and severe, as evidenced by observations and model simulations. While it is important to investigate how these intensifying heavy rainfall events affect current engineering standards, fewer studies have contextualized how warming impacts the most extreme rainfall from a single storm event relative to historical heavy downpours. In this study, we focused on the daily extreme rainfall associated with the extratropical transition of Hurricane Ida (2021), particularly over the northeastern United States—some of which exceeded the commonly used hydrologic design criteria for a 100-yr storm. Using a high-resolution atmospheric model simulation, we investigated how continued warming may influence the frequency of such daily rain rates. Under a high-emission scenario, these events are projected to become up to 5 times more likely at the end of the twenty-first century.

Dollan, Ishrat J↗
DOE OSTI2020

Influences of North Pacific Ocean Domain Extent on the Western U.S. Winter Hydroclimatology in Variable-Resolution CESM

Variable-resolution global climate models (VRGCMs) are a dynamical downscaling method that can reach spatiotemporal scales needed for regional climate assessments. Over the years, several users of VRGCMs have assumed where the location and extent of the refinement domain should be based on knowledge of the prevailing storm tracks and resolution dependence of important regional climate processes (e.g., atmospheric rivers [ARs] and orographic uplift), but the effect of high-resolution domain size and extent on the simulation of downstream hydroclimatic phenomena has not been systematically evaluated. Here, we use variable resolution in the Community Earth System Model (VR-CESM) to perform such a test. To do this, three VR-CESM grids were generated that span the entire, two thirds, and one third of the North Pacific and evaluated for a 30-year climatology using Atmospheric Model Intercomparison Project protocols. Simulations are compared with reanalysis products offshore (fifth-generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis [ERA5]) and onshore (Livneh, 2015, https://doi.org/10.1038/sdata.2015.42, and Parameter-elevation Regressions on Independent Slopes Model [PRISM]) of the western United States. The westward expansion of refinement domain influenced integrated vapor transport (IVT), which was generally high biased but minimally impacted AR characteristics. Due to slight differences in landfalling AR counts in the western United States, California winter precipitation generally improved with westward expansion of the refinement domain. Western U.S. mountain snowpack and surface temperatures were insensitive to refinement domain size and were more influenced by changes in topographic resolution and/or land surface model version. Here, given minimal dependence of simulated western U.S. hydroclimate on refinement domain size over the North Pacific, we advise future VR-CESM studies to focus grid resolution on better resolving land surface heterogeneity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

A Multiyear Dataset of SSM/I-Derived Global Ocean Surface Turbulent Fluxes

The surface turbulent fluxes of momentum, latent heat, and sensible heat over global oceans are essential to weather, climate and ocean problems. Evaporation is a key component of the hydrological cycle and the surface heat budget, while the wind stress is the major forcing for driving the oceanic circulation. The global air-sea fluxes of momentum, latent and sensible heat, radiation, and freshwater (precipitation-evaporation) are the forcing for driving oceanic circulation and, hence, are essential for understanding the general circulation of global oceans. The global air-sea fluxes are required for driving ocean models and validating coupled ocean-atmosphere global models. We have produced a 7.5-year (July 1987-December 1994) dataset of daily surface turbulent fluxes over the global oceans from the Special Sensor microwave/Imager (SSM/I) data. Daily turbulent fluxes were derived from daily data of SSM/I surface winds and specific humidity, National Centers for Environmental Prediction (NCEP) sea surface temperatures, and European Centre for Medium-Range Weather Forecasts (ECMWF) air-sea temperature differences, using a stability-dependent bulk scheme. The retrieved instantaneous surface air humidity (with a 25-km resolution) validated well with that of the collocated radiosonde observations over the global oceans. Furthermore, the retrieved daily wind stresses and latent heat fluxes were found to agree well with that of the in situ measurements (IMET buoy, RV Moana Wave, and RV Wecoma) in the western Pacific warm pool during the TOGA COARE intensive observing period (November 1992-February 1993). The global distributions of 1988-94 seasonal-mean turbulent fluxes will be presented. In addition, the global distributions of 1990-93 annual-means turbulent fluxes and input variables will be compared with those of UWM/COADS covering the same period. The latter is based on the COADS (comprehensive ocean-atmosphere data set) and is recognized to be one of the best climatological analyses of fluxes derived from ship observations.

Chou, Shu-Hsien↗
NASA NTRS2008

Surface Meteorology and Solar Energy (SSE) Data Release 5.1

The Surface meteorology and Solar Energy (SSE) data set contains over 200 parameters formulated for assessing and designing renewable energy systems.The SSE data set is formulated from NASA satellite- and reanalysis-derived insolation and meteorological data for the 10-year period July 1983 through June 1993. Results are provided for 1 degree latitude by 1 degree longitude grid cells over the globe. Average daily and monthly measurements for 1195 World Radiation Data Centre ground sites are also available. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1983-07-01; Stop_Date=1993-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree].

Arctic heating↗
NASA NTRS2007

Comparison of Different Global Information Sources Used in Surface Radiative Flux Calculation: Radiative Properties of the Surface

Direct estimates of surface radiative fluxes that resolve regional and weather-scale variabilty over the whole globe with reasonable accuracy have only become possible with the advent of extensive global, mostly satellite, datasets within the past couple of decades. The accuracy of these fluxes, estimated to be about 10-15 W per square meter is largely limited by the accuracy of the input datasets. The leading uncertainties in the surface fluxes are no longer predominantly induced by clouds but are now as much associated with uncertainties in the surface and near-surface atmospheric properties. This study presents a fuller, more quantitative evaluation of the uncertainties for the surface albedo and emissivity and surface skin temperatures by comparing the main available global datasets from the Moderate-Resolution Imaging Spectroradiometer product, the NASA Global Energy and Water Cycle Experiment Surface Radiation Budget project, the European Centre for Medium-Range Weather Forecasts, the National Aeronautics and Space Administration, the National Centers for Environmental Prediction, the International Satellite Cloud Climatology Project (ISCCP), the Laboratoire de Meteorologie Dynamique, NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer project, NOAA Optimum Interpolation Sea Surface Temperature Analysis and the Tropical Rainfall Measuring Mission (TRMM) Microwave Image project. The datasets are, in practice, treated as an ensemble of realizations of the actual climate such that their differences represent an estimate of the uncertainty in their measurements because we do not possess global truth datasets for these quantities. The results are globally representative and may be taken as a generalization of our previous ISCCP-based uncertainty estimates for the input datasets. Surface properties have the primary role in determining the surface upward shortwave (SW) and longwave (LW) flux. From this study, the following conclusions are obtained. Although land surface albedos in the near near-infrared remain poorly constrained (highly uncertain), they do not cause too much error in total surface SW fluxes; the more subtle regional and seasonal variations associated with vegetation and snow are still on doubt. The uncertainty of the broadband black-sky SW albedo for land surface from this study is about 7%, which can easily induce 5-10 W per square meter uncertainty in (upwelling) surface SW flux estimates. Even though available surface (broadband) LW emissivity datasets differ significantly (3%-5% uncertainty), this disagreement is confined to wavelengths greater than 20 micrometers so that there is little practical effect (1-3 W per square meters) on the surface upwelling LW fluxes. The surface skin temperature is one of two leading factors that cause problems with surface LW fluxes. Even though the differences among the various datasets are generally only 2-4 K, this can easily cause 10-15 W per square meter uncertainty in calculated surface (upwelling) LW fluxes. Significant improvements could be obtained for surface LW flux calculations by improving the retrievals of (in order of decreasing importance): (1) surface skin temperature, (2) surface air and near-surface-layer temperature, (3) column precipitable water amount and (4) broadband emissivity. And for surface SW fluxes, improvements could be obtained (excluding improved cloud treatment) by improving the retrievals of (1) aerosols (from our sensitivity studies but not discussed in this work), and (2) surface (black-sky) albedo, of which, NIR part of the spectrum has much larger uncertainty.

Zhang, Yuanchong↗