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NASA NTRS2002

1999 NCCS Highlights

The NASA Center for Computational Sciences (NCCS) is a high-performance scientific computing facility operated, maintained and managed by the Earth and Space Data Computing Division (ESDCD) of NASA Goddard Space Flight Center's (GSFC) Earth Sciences Directorate. The mission of the NCCS is to advance leading-edge science by providing the best people, computers, and data storage systems to NASA's Earth and space sciences programs and those of other U.S. Government agencies, universities, and private institutions. Among the many computationally demanding Earth science research efforts supported by the NCCS in Fiscal Year 1999 (FY99) are the NASA Seasonal-to-Interannual Prediction Project, the NASA Search and Rescue Mission, Earth gravitational model development efforts, the National Weather Service's North American Observing System program, Data Assimilation Office studies, a NASA-sponsored project at the Center for Ocean-Land-Atmosphere Studies, a NASA-sponsored microgravity project conducted by researchers at the City University of New York and the University of Pennsylvania, the completion of a satellite-derived global climate data set, simulations of a new geodynamo model, and studies of Earth's torque. This document presents highlights of these research efforts and an overview of the NCCS, its facilities, and its people.

Bennett, Jerome↗

NASA NTRS1999

El Nino and the Global Ocean Observing System

Until a decade ago, an often-quoted expression in oceanography is that very few observations are recorded throughout the ocean. Now, the sentiment is no longer valid in the uppermost 10% of the tropical Pacific Ocean nor at the surface of the global ocean. One of the remarkable legacies of the 1985-1994 Tropical Oceans Global Atmosphere (TOGA) Program is an in situ marine meteorological and upper oceanographic measurement array throughout the equatorial Pacific to monitor the development and maintenance of El Nino episodes. The TOGA Observing System, which initially consisted of moored- and drifting-buoy arrays, a network of commercial ships, and coastal and island stations, now includes a constellation of satellites and data-assimilating models to simulate subsurface oceanographic conditions. The El Nino and La Nina tropical Pacific Ocean observing system represents the initial phase of an integrated global ocean observing system. Remarkable improvements have been made in ocean model simulation of subsurface currents, but some problems persist. For example, the simulation of the South Equatorial Current (SEC) remains an important challenge in the 2S-2N Pacific equatorial wave guide. During El Nino the SEC at the equator is reduced and sometimes the direction is reversed, becoming eastward. Both conditions allow warm water stored in the western Pacific to invade the eastern region, creating an El Nino episode. Assimilation of data is a tenet of faith to correct simulation errors caused by deficiencies in surface fluxes (especially wind stress) and parameterizations of subgrid-scale physical processes. In the first of two numerical experiments, the Pacific SEC was simulated with and without assimilation of subsurface temperature data. Along the equator, a very weak SEC occurred throughout the eastern Pacific, independent of assimilation of data. However, as displayed in the diagram, in the western Pacific there was no satisfactory agreement between the two simulations. To help determine reliability of the simulated SEC in the western Pacific, current measurements recorded during the 9-19 October 1994 voyage of the French research vessel L'Atalante are also shown in the diagram. With data assimilation, the simulated SEC was in much better agreement with L'Atalante observations. The simulated SEC with data assimilation was far from perfect, in part because of the sparsity of subsurface temperature observations. In the next experiment, TOPEX/POSEIDON sea surface height data in combination with subsurface temperatures will be assimilated to assess further improvement of the simulation of the SEC.

Halpern, David↗

NASA NTRSDate not supplied

Assimilation of Satellite Sea Surface Temperature and Salinity Using the Local Ensemble Transform Kalman Filter Applied to the NASA GEOS Earth System Model

The NASA GEOS-S2S-2 system (Molod et al., 2020) uses the SST relaxation with the Level-4 SST products while the latest GEOS GEOS-S2S-3 system directly assimilates the Level-4 SST in the data assimilation step, yet no study has been conducted to understand the impact of this switch on the GEOS coupled analysis and short-range forecast. In addition, the GEOS-S2S-2/3 systems use the ocean model MOM5. Recently, the ocean model MOM6 is added to the GEOS ESM. This poster presents results of the first coupling of the ensemble data assimilation system to the GEOS MOM6.

Cheng Da↗

NASA NTRS2016

Ensemble-Based Assimilation of Aerosol Observations in GEOS-5

MERRA-2 is the latest Aerosol Reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO) from 1979 to present. This reanalysis is based on a version of the GEOS-5 model radiatively coupled to GOCART aerosols and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from AVHRR over ocean, MODIS sensors on both Terra and Aqua satellites, MISR over bright surfaces and AERONET data. In order to assimilate lidar profiles of aerosols, we are updating the aerosol component of our assimilation system to an Ensemble Kalman Filter (EnKF) type of scheme using ensembles generated routinely by the meteorological assimilation. Following the work performed with the first NASA's aerosol reanalysis (MERRAero), we first validate the vertical structure of MERRA-2 aerosol assimilated fields using CALIOP data over regions of particular interest during 2008.

LDE↗

NASA NTRS1984

The influence of actual and apparent geoid error on ocean analysis and prediction

The radar altimeter is the only satellite remote sensor with a proven capability for synoptically measuring an integral property of the dynamic ocean on a near global, all weather basis. Because acquisition of global, in situ ocean data with space/time resolution adequate to describe dynamically important ocean features is practically impossible, any attempt to develop a global ocean monitoring and forecasting system will rely heavily on altimetric data for initialization and updating. Maximizing useful information from the altimeter while minimizing error sources and developing methods for assimilating altimeter data into dynamical models are, therefore, vital areas for research and development. The limits imposed on ocean prediction by errors in the geoid or apparent errors associated with ground track variations near strong geopotential gradients are examined.

Thompson, J. D.↗

NASA NTRS2012

Using the NASA Giovanni DICCE Portal to Investigate Land-Ocean Linkages with Satellite and Model Data

Data-enhanced Investigations for Climate Change Education (DICCE), a NASA climate change education project, employs the NASA Giovanni data system to enable teachers to create climate-related classroom projects using selected satellite and assimilated model data. The easy-to-use DICCE Giovanni portal (DICCE-G) provides data parameters relevant to oceanic, terrestrial, and atmospheric processes. Participants will explore land-ocean linkages using the available data in the DICCE-G portal, in particular focusing on temperature, ocean biology, and precipitation variability related to El Ni?o and La Ni?a events. The demonstration includes the enhanced information for educators developed for the DICCE-G portal. The prototype DICCE Learning Environment (DICCE-LE) for classroom project development will also be demonstrated.

Acker, James G.↗

NASA NTRS1992

GEWEX

The GEWEX (Global Energy and Water cycle EXperiment) objectives are as follows: to determine the hydrologic cycle and energy fluxes by means of global measurements of observable atmospheric and surface properties; to model the global hydrologic cycle and its effects on the atmosphere and oceans; to foster the development of observing techniques and data management and assimilation systems; and to develop the ability to predict the variations of global and regional hydrologic processes and water resources, and their response to environmental change.

Source record↗

NASA NTRS1998

The Impact of NASA Scatterometer Data on GEOS-2 Analyses and Forecasts

Experiments with the evolving GEOS-2 data assimilation system (DAS) delineate the impact of NASA scatterometer (NSCAT) data on ocean surface analysis and numerical weather prediction (NWP). Extensions and refinements of the DAS to account for the characteristics of NSCAT data produced better results than were obtained with the GEOS-1 DAS. The two key extensions are to increase the vertical influence of the surface wind data and to take proper account of the time difference between the observation and the analysis. The results of these experiments show that surface wind analyses are improved by NSCAT data. This is seen in subjective evaluation of synoptic cases and in forecast impacts. NSCAT data have a very significant positive impact on the GEOS-2 forecasts in the southern hemisphere (SH). In the northern hemisphere (NH) the overall statistics show a modest positive impact. However, on a case by case basis, in the NH, the impact is generally neutral or significantly positive. The GEOS-2 results are compared to results obtained using the GEOS-1 DAS and using the 1995 National Center for Environmental Prediction (NCEP95) DAS. The GEOS-2 control forecasts are more accurate than those of GEOS-1. All three impact experiments show a large positive impact in the SH. In the NH, both the GEOS-2 and the NCEP95 NSCAT impacts are positive while the GEOS-1 impact is neutral.

Atlas, R.↗

NASA NTRS2003

Role of the Polar Oceans in Global Climate

The project focused on ice-ocean model development and in particular on the assimilation of ice motion data and ice concentration data into both regional and global models. Many of the resulting publications below deal with improvements made in the physics treated by the model and the procedures for assimilating data. Several papers examine how the ability of the model to simulate the past behavior of the ice cover, especially to represent the ice thickness and ice deformation, is improved by data assimilation. A second aspect of the work involved interpretation of modeled behavior. Resulting papers treat the decline of arctic ice thickness over the last thirty years, and how that decline was caused by a slight warming of the near-surface atmosphere, and also how large variation in ice thickness are due to changes in wind patterns associated with a well- known oscillation of the atmospheric circulation. The research resulted in over 20 published papers on these topics.

Rothrock, D. A.↗

NASA NTRS2011

The Global Observing System in the Assimilation Context

Weather and climate analyses and predictions all rely on the global observing system. However, the observing system, whether atmosphere, ocean, or land surface, yields a diverse set of incomplete observations of the different components of Earth s environment. Data assimilation systems are essential to synthesize the wide diversity of in situ and remotely sensed observations into four-dimensional state estimates by combining the various observations with model-based estimates. Assimilation, or associated tools and products, are also useful in providing guidance for the evolution of the observing system of the future. This paper provides a brief overview of the global observing system and information gleaned through assimilation tools, and presents some evaluations of observing system gaps and issues.

Reinecker, Michele M.↗

NASA NTRS2002

Project to Interface Climate Modeling on Global and Regional Scales with Earth Observing (EOS) Observations

This ten-year NASA IDS project began in 1990. Its initial work plan adopted the NASA provided timeline that data would become available for new Earth Observing System (EOS) platforms beginning in 1995. Over its first phase, it was based at NCAR, which had submitted the original proposal and involved activities of a substantial number of co-investigators at NCAR who engaged in research over several areas related to the observations expected to be received from the EOS platforms. Their focus was the theme of use of EOS data for improving climate models for projecting global change. From the climate system viewpoint, the IDS addressed land, clouds-hydrological cycle, radiative fluxes and especially aerosol impacts, ocean and sea-ice, and stratosphere. Other research addressed issues of data assimilation, diagnostic analyses, and data set development from current satellite systems, especially use of SAR data for climate models.

Dickinson, Robert E.↗

NASA NTRS2014

Soil Moisture Data Assimilation in the NASA Land Information System for Local Modeling Applications and Improved Situational Awareness

As part of the NASA Soil Moisture Active Passive (SMAP) Early Adopter (EA) program, the NASA Shortterm Prediction Research and Transition (SPoRT) Center has implemented a data assimilation (DA) routine into the NASA Land Information System (LIS) for soil moisture retrievals from the European Space Agency's Soil Moisture Ocean Salinity (SMOS) satellite. The SMAP EA program promotes application‐driven research to provide a fundamental understanding of how SMAP data products will be used to improve decision‐making at operational agencies. SPoRT has partnered with select NOAA/NWS Weather Forecast Offices (WFOs) that use output from a real‐time regional configuration of LIS, without soil moisture DA, to initialize local numerical weather prediction (NWP) models and enhance situational awareness. Improvements to local NWP with the current LIS have been demonstrated; however, a better representation of the land surface through assimilation of SMOS (and eventually SMAP) retrievals is expected to lead to further model improvement, particularly during warm‐season months. SPoRT will collaborate with select WFOs to assess the impact of soil moisture DA on operational forecast situations. Assimilation of the legacy SMOS instrument data provides an opportunity to develop expertise in preparation for using SMAP data products shortly after the scheduled launch on 5 November 2014. SMOS contains a passive L‐band radiometer that is used to retrieve surface soil moisture at 35‐km resolution with an accuracy of 0.04 cu cm cm (exp -3). SMAP will feature a comparable passive L‐band instrument in conjunction with a 3‐km resolution active radar component of slightly degraded accuracy. A combined radar‐radiometer product will offer unprecedented global coverage of soil moisture at high spatial resolution (9 km) for hydrometeorological applications, balancing the resolution and accuracy of the active and passive instruments, respectively. The LIS software framework manages land surface model (LSM) simulations and includes an Ensemble Kalman Filter for conducting land surface DA. SPoRT has added a module to read, quality‐control and bias‐correct swaths of Level II SMOS soil moisture retrievals prior to assimilation within LIS. The impact of SMOS DA is being tested using the Noah LSM. Experiments are being conducted to examine the impacts of SMOS soil moisture DA on the resulting LISNoah fields and subsequent NWP simulations using the Weather Research and Forecasting (WRF) model initialized with LIS‐Noah output. LIS‐Noah soil moisture will be validated against in situ observations from Texas A&M's North American Soil Moisture Database to reveal the impact and possible improvement in soil moisture trends through DA. WRF model NWP case studies will test the impacts of DA on the simulated near‐surface and boundary‐layer environments, and precipitation during both quiescent and disturbed weather scenarios. Emphasis will be placed on cases with large analysis increments, especially due to contributions from regional irrigation patterns that are not represented by precipitation input in the baseline LIS‐Noah run. This poster presentation will describe the soil moisture DA methodology and highlight LIS‐Noah and WRF simulation results with and without assimilation.

Case, Jonathan L.↗

NASA NTRS2017

Simulating PACE Global Ocean Radiances

The NASA PACE mission is a hyper-spectral radiometer planned for launch in the next decade. It is intended to provide new information on ocean biogeochemical constituents by parsing the details of high resolution spectral absorption and scattering. It is the first of its kind for global applications and as such, poses challenges for design and operation. To support pre-launch mission development and assess on-orbit capabilities, the NASA Global Modeling and Assimilation Office has developed a dynamic simulation of global water-leaving radiances, using an ocean model containing multiple ocean phytoplankton groups, particulate detritus, particulate inorganic carbon (PIC), and chromophoric dissolved organic carbon (CDOC) along with optical absorption and scattering processes at 1 nm spectral resolution. The purpose here is to assess the skill of the dynamic model and derived global radiances. Global bias, uncertainty, and correlation are derived using available modern satellite radiances at moderate spectral resolution. Total chlorophyll, PIC, and the absorption coefficient of CDOC (aCDOC), are simultaneously assimilated to improve the fidelity of the optical constituent fields. A 5-year simulation showed statistically significant (P < 0.05) comparisons of chlorophyll (r = 0.869), PIC (r = 0.868), and a CDOC (r =0.890) with satellite data. Additionally, diatoms (r = 0.890), cyanobacteria (r = 0.732), and coccolithophores (r = 0.716) were significantly correlated with in situ data. Global assimilated distributions of optical constituents were coupled with a radiative transfer model (Ocean-Atmosphere Spectral Irradiance Model, OASIM) to estimate normalized water-leaving radiances at 1 nm for the spectral range 250-800 nm. These unassimilated radiances were within 0.074 mW/sq cm/micron/sr of MODIS-Aqua radiances at 412, 443, 488, 531, 547, and 667 nm. This difference represented a bias of 10.4% (model low). A mean correlation of 0.706 (P < 0.05) was found with global distributions of MODIS radiances. These results suggest skill in the global assimilated model and resulting radiances. The reported error characterization suggests that the global dynamical simulation can support some aspects of mission design and analysis. For example, the high spectral resolution of the simulation supports investigations of band selection. The global nature of the radiance representations supports investigations of satellite observing scenarios. Global radiances at bands not available in current and past missions support investigations of mission capability. PACE, ocean color, water-leaving radiances, biogeochemical model, radiative transfer model

PIC↗

NASA NTRS2000

The Correlation Between Atmospheric Dust Deposition to the Surface Ocean and SeaWiFS Ocean Color: A Global Satellite-Based Analysis

Since the atmospheric deposition of iron has been linked to primary productivity in various oceanic regions, we have conducted an objective study of the correlation of dust deposition and satellite remotely sensed surface ocean chlorophyll concentrations. We present a global analysis of the correlation between atmospheric dust deposition derived from a satellite-based 3-D atmospheric transport model and SeaWiFs estimates of ocean color. We use the monthly mean dust deposition fields of Ginoux et al. which are based on a global model of dust generation and transport. This model is driven by atmospheric circulation from the Data Assimilation Office (DAO) for the period 1995-1998. This global dust model is constrained by several satellite estimates of standard circulation characteristics. We then perform an analysis of the correlation between the dust deposition and the 1998 SeaWIFS ocean color data for each 2.0 deg x 2.5 deg lat/long grid point, for each month of the year. The results are surprisingly robust. The region between 40 S and 60 S has correlation coefficients from 0.6 to 0.95, statistically significant at the 0.05 level. There are swaths of high correlation at the edges of some major ocean current systems. We interpret these correlations as reflecting areas that have shear related turbulence bringing nitrogen and phosphorus from depth into the surface ocean, and the atmospheric supply of iron provides the limiting nutrient and the correlation between iron deposition and surface ocean chlorophyll is high. There is a region in the western North Pacific with high correlation, reflecting the input of Asian dust to that region. The southern hemisphere has an average correlation coefficient of 0.72 compared that in the northern hemisphere of 0.42 consistent with present conceptual models of where atmospheric iron deposition may play a role in surface ocean biogeochemical cycles. The spatial structure of the correlation fields will be discussed within the context of guiding the design of field programs.

Erickson, D. J., III↗

NASA NTRS2007

Sampling Biases in MODIS and SeaWiFS Ocean Chlorophyll Data

Although modem ocean color sensors, such as MODIS and SeaWiFS are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a "truth field", which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25deg longitude by 0.67deg latitude. The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and >5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (<3%) in the midlatitudes (between -40deg and 40deg). Low interannual variability in the global annual mean sampling biases suggested that global scale trend analyses were valid. High latitude biases were much higher than the global annual means, up to 20% as a basin annual mean, and over 80% in some months. This was the result of the high solar zenith angle exclusion in the processing algorithms. Only data where the solar angle is <75deg are permitted, in contrast to the assimilation which samples regularly over the entire area and month. High solar zenith angles do not facilitate phytoplankton photosynthesis and consequently low chlorophyll concentrations occurring here are missed by the data sets. Ocean color sensors selectively sample in locations and times of favorable phytoplankton growth, producing overestimates of chlorophyll. The biases derived from lack of sampling in the high latitudes varied monthly, leading to artifacts in the apparent seasonal cycle from ocean color sensors. A false secondary peak in chlorophyll occurred in May-August, which resulted from lack of sampling in the Antarctic.

Gregg, Watson W.↗

NASA NTRS2007

Salinity Remote Sensing and the Study of the Global Water Cycle

The SMOS and AquariusISAC-D satellite missions will begin a new era to map the global sea surface salinity (SSS) field and its variability from space within the next twothree years. They will provide critical data needed to study the interactions between the ocean circulation, global water cycle and climate. Key scientific issues to address are (1) mapping large expanses of the ocean where conventional SSS data do not yet exist, (2) understanding the seasonal and interannual SSS variations and the link to precipitation, evaporation and sea-ice patterns, (3) links between SSS and variations in the oceanic overturning circulation, (4) air-sea coupling processes in the tropics that influence El Nino, and (4) closing the marine freshwater budget. There is a growing body of oceanographic evidence in the form of salinity trends that portend significant changes in the hydrologic cycle. Over the past several decades, highlatitude oceans have become fresher while the subtropical oceans have become saltier. This change is slowly spreading into the subsurface ocean layers and may be affecting the strength of the ocean's therrnohaline overturning circulation. Salinity is directly linked to the ocean dynamics through the density distribution, and provides an important signature of the global water cycle. The distribution and variation of oceanic salinity is therefore attracting increasing scientific attention due to the relationship to the global water cycle and its influence on circulation, mixing, and climate processes. The oceans dominate the water cycle by providing 86% of global surface evaporation (E) and receiving 78% of global precipitation (P). Regional differences in E-P, land runoff, and the melting or freezing of ice affect the salinity of surface water. Direct observations of E-P over the ocean have large uncertainty, with discrepancies between the various state-of-the-art precipitation analyses of a factor of two or more in many regions. Quantifying the climatic influence of the oceanic water cycle requires more accurately resolving the net air-sea water flux. Measuring global SSS trends on seasonal to interannual timescales by satellite is fundamental to this problem because the SSS trends represent detectable time-integrated signals of the variable marine hydrological cycle. Satellite measurements, coupled with an array of in situ observations, will provide global synoptic SSS fields for the first time history. These data will provide a strong constraint on climate models and data assimilation efforts, which must properly represent the freshwater budget in terms of E-P, ocean advection and surface layer mixing in order to accurately simulate the true ocean state. The SSS fields will allow us to quantify the covariability between the SSS and the strong seasonal E-P cycle in the tropics and high latitudes. Field measurement campaigns to exploit satellite and in situ measurements to close the seasonal E-P cycle over an ocean region are being considered. Lastly the satellite systems will monitor and trace the large long-lived SSS anomalies from year to year that have the potential to influence El Nino and the large scale ocean circulation.

Lagerloef, G. S. E.↗

NASA NTRS2001

The Aquarius Mission: Sea Surface Salinity from Space

Aquarius is a new satellite mission concept to study the impact of the global water cycle on the ocean, including the response of the ocean to buoyancy forcing and the subsequent feedback of the ocean on the climate. The measurement objective of Aquarius is sea surface salinity, which reflects the concentration of freshwater at the ocean surface. Salinity affects the dielectric constant of sea water and, consequently, the radiometric emission of the sea surface to space. Rudimentary space observations with an L-band radiometer were first made from Skylab in the mid-70s and numerous aircraft missions of increasing quality and improved technology have been conducted since then. Technology is now available to carry out a global mission, which includes both an accurate L band (1.413 Ghz) radiometer and radar system in space and a global array of in situ observations for calibration and validation, in order to address key NASA Earth Science Enterprise questions about the global cycling of water and the response of the ocean circulation to climate change. The key scientific objectives of Aquarius examine the cycling of water at the ocean's surface, the response of the ocean circulation to buoyancy forcing, and the impact of buoyancy forcing on the ocean's thermal feedback to the climate. Global surface salinity will also improve our ability to model the surface solubility chemistry needed to estimate the air-sea exchange of CO2. In order to meet these science objectives, the NASA Salinity Sea Ice Working Group over the past three years has concluded that the mission measurement goals should be better than 0.2 practical salinity units (psu) accuracy, 100 km resolution, and weekly to revisits. The Aquarius mission proposes to meet these measurement requirements through a real aperture dual-polarized L band radiometer and radar system. This system can achieve the less than 0.1 K radiometric temperature measurement accuracy that is required. A 3 m antenna at approx. 600km altitude in a sun-synchronous orbit and 300 km swath can provide the desired 100 km resolution global coverage every week. Within this decade, it may be possible to combine satellite sea surface salinity measurements with ongoing satellite observations of temperature, surface height, air-sea fluxes; vertical profiles of temperature and salinity from the Argo program; and modern ocean/atmosphere modeling and data assimilation tools, in order to finally address the complex influence of buoyancy on the ocean circulation and climate.

Koblinsky, Chester↗

NASA NTRS2004

NASA GEOS-3/TRMM Re-analysis: Capturing Observed Tropical Rainfall Variability in Global Analysis for Climate Research

Understanding climate variability over a wide range of space-time scales requires a comprehensive description of the earth system. Global analyses produced by a fixed assimilation system (i.e., re-analyses) - as their quality continues to improve - have the potential of providing a vital tool for meeting this challenge. But at the present time, the usefulness of re-analyses is limited by uncertainties in such basic fields as clouds, precipitation, and evaporation - especially in the tropics, where observations are relatively sparse. Analyses of the tropics have long been shown to be sensitive to. the treatment of cloud precipitation processes, which remains a major source of uncertainty in current models. Yet, for many climate studies it is crucial that analyses can accurately reproduce the observed rainfall intensity and variability since a small error of 1 mm/d in surface rain translates into an error of approx. 30 W/sq m in energy (latent heat) flux. Currently, discrepancies between the observed and analyzed monthly-mean rain rates averaged to 100 km x 100 km resolution can exceed 4 mm/d (or 120 W/sq m ), compared to uncertainties in surface radiative fluxes of approx. 10-20 W/sq m . Improving precipitation in analyses would reduce a major source of uncertainty in the global energy budget. Uncertainties in tropical precipitation have also been a major impediment in understanding how the tropics interact with other regions, including the remote response to El Nino/Southern Oscillation (ENSO) variability on interannual time scales, the influence of Madden-Julian Oscillation (MJO) and monsoons on intraseasonal time scales. A global analysis that can replicate the observed precipitation variability together with physically consistent estimates of other atmospheric variables provides the key to breaking this roadblock. NASA Goddard Space Flight Center has been exploring the use of satellite-based microwave rainfall measurements in improving global analyses and has recently produced a multi-year, 1 x 1 TRMM re-analysis , which assimilates 6-hourly TMI and SSM/I surface rain rates over tropical oceans using a ID variational continuous assimilation (VCA) procedure in the GEOS-3 global data assimilation system. The analysis period extends from 1 November 1997 through 3 1 December 2002. The goal is to produce a multi-year global analysis that is dynamically consistent with available tropical precipitation observations for the community to assess its utility in climate applications and identify areas for further improvements. A distinct feature of the GEOS-3RRMh4 re-analysis is that its precipitation analysis is not derived from a short-term forecast (as done in most operational systems) but is given by a time- continuous model integration constrained by precipitation observations within a 6-h analysis window, while the wind, temperature, and pressure fields are allowed to directly respond to the improved precipitation and associated latent heating structures within the same analysis window. In this talk, I will assess the impact VCA precipitation assimilation on analyses of climate signals ranging from a few weeks to interannual time scales and compare results against other operational and reanalysis products.

Hou, Arthur Y.↗