Results for “Assimilation” — Page 2

NASA NTRS1999

Assimilation of SeaWinds Scatterometer Data in the GEOS Data Assimilation System

The first SeaWinds scatterometer was launched in to space aboard the Quikscat satellite on June 19, 1999 at 7:15 p.m. PDT. Flying in a near polar orbit 800 km above the earth's surface, SeaWinds uses an advanced scatterometer design to measure surface wind velocity over 90 percent of the ice free oceans ever 24 hours. This first SeaWinds mission is designed to replace the NASA Scatterometer (NSCAT) which ceased providing wind velocity data when the ADEOS I satellite failed. A second SeaWinds is scheduled to be launched late in 2000 aboard ADEOS II. Previous scatterometer assimilation experiments conducted by the NASA Data Assimilation Office, using both ERS and NSCAT wind observations, have demonstrated considerable potential for this type of data to improve both atmospheric analyses and forecasts, however much of the smaller scale information content of the scatterometer data could not be taken into account in the early coarse resolution versions of the Goddard (GEOS) Data Assimilation System (DAS) or in operational data assimilation systems. In this paper, we will describe data assimilation experiments in which the new higher resolution versions of the GOES DAS are used to assimilate SeaWinds scatterometer winds. Following a brief discussion of the SeaWinds design and the methodology used to assimilate scatterometer data in the GOES DAS, the quality of the SeaWinds data and the impact of SeaWinds on GOES analyses and forecasts at different resolutions will be presented.

Atlas, Robert↗

NASA NTRS2016

Assimilation of Gridded GRACE Terrestrial Water Storage Estimates in the North American Land Data Assimilation System

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

Kumar, Sujay V.↗

NASA NTRSDate not supplied

GEOS Constituent Data Assimilation beyond Aura MLS: Assimilating NASA SAGE III/ISS profiles of stratospheric water vapor

Water vapor in the lower stratosphere is an important trace gas for atmospheric chemistry and radiative budget with direct impact on climate. During the last quarter of the 20th century, the Stratospheric Aerosol and Gas Experiment (SAGE) missions were crucial in monitoring the stratospheric ozone loss and the subsequent recovery as well as trends in water vapor linked to surface temperature trends. The SAGE III instrument aboard the International Space Station (ISS) continues the SAGE mission record, with high vertical resolution profiles of water vapor (and other constituents) since mid-2017. The NASA GEOS Earth system model has the capability to assimilate multi-constituents from ground and space-based instruments using the GEOS Constituent Data Assimilation System (CoDAS). Reanalysis products of stratospheric water vapor without data constraints are historically poor, and two recent reanalyses which assimilated stratospheric constituents observed by the Microwave Limb Sounder (MLS) improve the representation of stratospheric composition, including water vapor, when compared against independent observations. The MLS instrument is on NASA's Aura satellite which is expected to be decommissioned in the coming years. Here we demonstrate that while the number of solar occultation observations a day from SAGE III/ISS is about 1 % of the total number of profiles observed globally by MLS, the chemical timescales of water vapor in the lower stratosphere are long enough that the SAGE III/ISS data can provide a useful constraint on the assimilated product. Analyzed water vapor fields from assimilating only SAGE III/ISS water vapor profiles agree well with independent stratospheric observations, including when compared against the frost-point hygrometers at the three NOAA stations. Over the five-year period from 2017 through 2022, the analyzed water vapor using SAGE III/ISS captures many of the features seen with the assimilation of MLS observations. Note, this ability is reduced over regions where the SAGE III/ISS instrument provides infrequent or no observations (associated with the orbit of the ISS), as highlighted in the case study period following the eruption of the tropical underwater volcano Hunga Tonga in early 2022. Nevertheless, there is a clear benefit to the assimilation of stratospheric water vapor from SAGE III/ISS observations, allowing us to continue to monitor stratospheric composition for climate assessments following the impending loss of Aura.

K. Emma Knowland↗

NASA NTRSDate not supplied

GEOS Constituent Data Assimilation Beyond Aura MLS: Assimilating NASA SAGE III/ISS Profiles of Stratospheric Water Vapor

Water vapor in the lower stratosphere is an important trace gas for atmospheric chemistry and radiative budget with direct impact on climate. During the last quarter of the 20th century, the Stratospheric Aerosol and Gas Experiment (SAGE) missions were crucial in monitoring the stratospheric ozone loss and the subsequent recovery as well as trends in water vapor linked to surface temperature trends. The SAGE III instrument aboard the International Space Station (ISS) continues the SAGE mission record, with high vertical resolution profiles of water vapor (and other constituents) since mid-2017. The NASA GEOS Earth system model has the capability to assimilate multi-constituents from ground and space-based instruments using the GEOS Constituent Data Assimilation System (CoDAS). Reanalysis products of stratospheric water vapor without data constraints are historically poor, and two recent reanalyses which assimilated stratospheric constituents observed by the Microwave Limb Sounder (MLS) improve the representation of stratospheric composition, including water vapor, when compared against independent observations. The MLS instrument is on NASA's Aura satellite which is expected to be decommissioned in the coming years. Here we demonstrate that while the number of solar occultation observations a day from SAGE III/ISS is about 1 % of the total number of profiles observed globally by MLS, the chemical timescales of water vapor in the lower stratosphere are long enough that the SAGE III/ISS data can provide a useful constraint on the assimilated product. Analyzed water vapor fields from assimilating only SAGE III/ISS water vapor profiles agree well with independent stratospheric observations, including when compared against the frost-point hygrometers at the three NOAA stations. Over the five-year period from 2017 through 2022, the analyzed water vapor using SAGE III/ISS captures many of the features seen with the assimilation of MLS observations. Note, this ability is reduced over regions where the SAGE III/ISS instrument provides infrequent or no observations (associated with the orbit of the ISS), as highlighted in the case study period following the eruption of the tropical underwater volcano Hunga Tonga in early 2022. Nevertheless, there is a clear benefit to the assimilation of stratospheric water vapor from SAGE III/ISS observations, allowing us to continue to monitor stratospheric composition for climate assessments following the impending loss of Aura.

K. Emma Knowland↗

NASA NTRSDate not supplied

GEOS Constituent Data Assimilation Beyond Aura MLS: Assimilating NASA SAGE III/ISS Profiles of Stratospheric Water Vapor

Water vapor in the lower stratosphere is an important trace gas for atmospheric chemistry and radiative budget with direct impact on climate. During the last quarter of the 20th century, the Stratospheric Aerosol and Gas Experiment (SAGE) missions were crucial in monitoring the stratospheric ozone loss and the subsequent recovery as well as trends in water vapor linked to surface temperature trends. The SAGE III instrument aboard the International Space Station (ISS) continues the SAGE mission record, with high vertical resolution profiles of water vapor (and other constituents) since mid-2017. The NASA GEOS Earth system model has the capability to assimilate multi-constituents from ground and space-based instruments using the GEOS Constituent Data Assimilation System (CoDAS). Reanalysis products of stratospheric water vapor without data constraints are historically poor, and two recent reanalyses which assimilated stratospheric constituents observed by the Microwave Limb Sounder (MLS) improve the representation of stratospheric composition, including water vapor, when compared against independent observations. The MLS instrument is on NASA's Aura satellite which is expected to be decommissioned in the coming years. Here we demonstrate that while the number of solar occultation observations a day from SAGE III/ISS is about 1 % of the total number of profiles observed globally by MLS, the chemical timescales of water vapor in the lower stratosphere are long enough that the SAGE III/ISS data can provide a useful constraint on the assimilated product. Analyzed water vapor fields from assimilating only SAGE III/ISS water vapor profiles agree well with independent stratospheric observations, including when compared against the frost-point hygrometers at the three NOAA stations. Over the five-year period from 2017 through 2022, the analyzed water vapor using SAGE III/ISS captures many of the features seen with the assimilation of MLS observations. Note, this ability is reduced over regions where the SAGE III/ISS instrument provides infrequent or no observations (associated with the orbit of the ISS), as highlighted in the case study period following the eruption of the tropical underwater volcano Hunga Tonga in early 2022. Nevertheless, there is a clear benefit to the assimilation of stratospheric water vapor from SAGE III/ISS observations, allowing us to continue to monitor stratospheric composition for climate assessments following the impending loss of Aura.

K. Emma Knowland↗

NASA NTRS2002

Surface Temperature Assimilation in the Global Land Data Assimilation System (GLDAS)

The Global Land Data Assimilation System (GLDAS) is a global land parameterization that uses prescribed meteorology as forcing in order to determine regular gridded land surface states (temperature and moisture) and other properties (e.g. water and heat fluxes). In the present experiment, the assimilation of surface skin temperature is incorporated into the land parameterizations. The meteorological forcing was derived from the Goddard Earth Observing System (GEOS-3) Data Assimilation System (DAS) for the full year of 1998 GLDAS can use several land parameterizations, but here we use the Mosaic land surface model and the Common Land Model (CLM). TOVS surface temperature observations are assimilated into GLDAS. The TOVS observations are less frequent that observations used in previous experiments (ISCCP). The purpose of this presentation is to evaluate the impact of the TOVS assimilation on both Mosaic and CLM. We will especially consider the impact of coarse temporal observations on the assimilation and bias correction.

Bosilovich, Michael G.↗

NASA NTRSDate not supplied

Satellite Data Assimilation in the GEOS Atmospheric Data Assimilation System

The Goddard Earth Observing System (GEOS) is used for weather, climate, and air quality forecasts and producing reanalysis datasets. Its atmospheric data assimilation system (ADAS) is a hybrid–4DEnVar system. Various satellite radiance, atmospheric motion vector (winds), aircraft, and convectional data are assimilated by this system to produce global atmospheric states. The GEOS ADAS is enhanced by considering correlated observational errors among infrared radiance observations and assimilating new microwave radiance data in all-sky conditions. Historical microwave radiance data are tested within the all-sky microwave radiance assimilation framework and show beneficial effect in the preparation of the next GEOS reanalysis product. In this seminar, I will present the procedures of to assimilate microwave radiance data in all-sky conditions in the GEOS-ADAS. I will also discuss our efforts to update GEOS ADAS by assimilating more satellite data such as geostationary infrared observations and new observations made by the Advanced Technology Microwave Sounder (ATMS) aboard NOAA 21 polar orbit satellite.

Jianjun Jin↗

DOE OSTI2018

A global coupled ensemble data assimilation system using the Community Earth System Model and the Data Assimilation Research Testbed

In this paper, we present a description of the CESM/DART ensemble coupled data assimilation (DA) system based on the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) assimilation software. The CESM/DART should be viewed as a flexible system to support the DA needs of the CESM research community and not as a static reanalysis product. In this implementation of the CESM/DART, conventional insitu observations of the ocean and atmosphere are assimilated into the respective component models of the CESM using a 30-member ensemble adjustment Kalman filter (EAKF). CESM/DART is run in a “weakly coupled” configuration wherein observations native to each climate system component only directly impact the state vector for that component. Information is passed between components indirectly through the short-term coupled model forecasts that provide the EAKF background ensemble. This system leverages previous ensemble DA development for the Community Atmosphere Model and Parallel Ocean Program models using the DART EAKF. The CESM/DART project is a step towards providing increasingly useful DA capabilities for the CESM research community. Results are presented for our prototype 12-year reanalysis, run from 1970 to mid 1982. Multiple lines of evidence demonstrate that the system is capable of constraining the CESM coupled model to simulate the historical variability of the climate system in the well-observed Northern Hemisphere. A collection of monthly average variables, climate mode indices, observation diagnostics and snapshots of synoptic weather in the ocean and atmosphere are compared to established datasets, showing especially good agreement in the Northern Hemisphere. A discussion of the CESM/DART as a modular, community facility and the benefits and challenges associated with this vision is also included.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

Variational assimilation of VAS data into a mesoscale model Assimilation method and sensitivity experiments

The assimilation of temperature and moisture gradient information from the Visible IR Spin-Scan Radiometer's Atmospheric Sounder (VAS) into a mesoscale model is presently undertaken by means of a variational method and followed by tests of the sensitivity of both diabatic and adiabatic versions of the model to VAS data assimilations for the case of July 20-21, 1981. The synoptic scale effects of the assimilation of VAS data are noted to be negligible; the greatest impact was instead on mesoscale forecasts of convective instability patterns. The additional assimilation of relative humidity gradients did not significantly change the patterns of the forecast instabilities. The greatest improvements from assimilation resulted from the resolution of the strong mesoscale temperature gradients by the asynoptic VAS data.

Cram, J. M.↗

NASA NTRS1995

Technical report series on global modeling and data assimilation. Volume 4: Documentation of the Goddard Earth Observing System (GEOS) data assimilation system, version 1

This report describes the analysis component of the Goddard Earth Observing System, Data Assimilation System, Version 1 (GEOS-1 DAS). The general features of the data assimilation system are outlined, followed by a thorough description of the statistical interpolation algorithm, including specification of error covariances and quality control of observations. We conclude with a discussion of the current status of development of the GEOS data assimilation system. The main components of GEOS-1 DAS are an atmospheric general circulation model and an Optimal Interpolation algorithm. The system is cycled using the Incremental Analysis Update (IAU) technique in which analysis increments are introduced as time independent forcing terms in a forecast model integration. The system is capable of producing dynamically balanced states without the explicit use of initialization, as well as a time-continuous representation of non- observables such as precipitation and radiational fluxes. This version of the data assimilation system was used in the five-year reanalysis project completed in April 1994 by Goddard's Data Assimilation Office (DAO) Data from this reanalysis are available from the Goddard Distributed Active Center (DAAC), which is part of NASA's Earth Observing System Data and Information System (EOSDIS). For information on how to obtain these data sets, contact the Goddard DAAC at (301) 286-3209, EMAIL daac@gsfc.nasa.gov.

Suarez, Max J.↗

NASA NTRS1995

Technical report series on global modeling and data assimilation. Volume 6: A multiyear assimilation with the GEOS-1 system: Overview and results

The Data Assimilation Office (DAO) at Goddard Space Flight Center has produced a multiyear global assimilated data set with version 1 of the Goddard Earth Observing System Data Assimilation System (GEOS-1 DAS). One of the main goals of this project, in addition to benchmarking the GEOS-1 system, was to produce a research quality data set suitable for the study of short-term climate variability. The output, which is global and gridded, includes all prognostic fields and a large number of diagnostic quantities such as precipitation, latent heating, and surface fluxes. Output is provided four times daily with selected quantities available eight times per day. Information about the observations input to the GEOS-1 DAS is provided in terms of maps of spatial coverage, bar graphs of data counts, and tables of all time periods with significant data gaps. The purpose of this document is to serve as a users' guide to NASA's first multiyear assimilated data set and to provide an early look at the quality of the output. Documentation is provided on all the data archives, including sample read programs and methods of data access. Extensive comparisons are made with the corresponding operational European Center for Medium-Range Weather Forecasts analyses, as well as various in situ and satellite observations. This document is also intended to alert users of the data about potential limitations of assimilated data, in general, and the GEOS-1 data, in particular. Results are presented for the period March 1985-February 1990.

Suarez, Max J.↗

NASA NTRS2014

The Impact of the Assimilation of Aquarius Sea Surface Salinity Data in the GEOS Ocean Data Assimilation System

Ocean salinity and temperature differences drive thermohaline circulations. These properties also play a key role in the ocean-atmosphere coupling. With the availability of L-band space-borne observations, it becomes possible to provide global scale sea surface salinity (SSS) distribution. This study analyzes globally the along-track (Level 2) Aquarius SSS retrievals obtained using both passive and active L-band observations. Aquarius alongtrack retrieved SSS are assimilated into the ocean data assimilation component of Version 5 of the Goddard Earth Observing System (GEOS-5) assimilation and forecast model. We present a methodology to correct the large biases and errors apparent in Version 2.0 of the Aquarius SSS retrieval algorithm and map the observed Aquarius SSS retrieval into the ocean models bulk salinity in the topmost layer. The impact of the assimilation of the corrected SSS on the salinity analysis is evaluated by comparisons with insitu salinity observations from Argo. The results show a significant reduction of the global biases and RMS of observations-minus-forecast differences at in-situ locations. The most striking results are found in the tropics and southern latitudes. Our results highlight the complementary role and problems that arise during the assimilation of salinity information from in-situ (Argo) and space-borne surface (SSS) observations

Salinty↗

NASA NTRS2021

Assimilation of SMAP Brightness Temperature Observations in the GEOS Land–Atmosphere Data Assimilation System

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ∼0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m^3 /m^3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2m_max ) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2m_max is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ∼700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ∼0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

microwave remote sensing↗

NASA NTRS2022

Assessment of Geo-Kompsat-2A Atmospheric Motion Vector Data and Its Assimilation Impact in the GEOS Atmospheric Data Assimilation System

Korea’s second geostationary meteorological satellite, Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose Satellite-2A, GK2A), was successfully launched on 4 December 2018. GK2Agenerates Atmospheric Motion Vectors (AMVs) every 10 min in the full disk area. This data hasbeen disseminated via Global Telecommunication System (GTS) since 25 October 2019. This articleevaluates the quality of GK2A AMVs in the Goddard Earth Observing System (GEOS) atmosphericdata assimilation system (ADAS). The data show slow wind speed biases at 200–300 hPa and 600–800hPa in the northern and southern hemispheres. These biases are caused by observation heightassignment errors near jet streams. The Equivalent Blackbody Temperature (EBBT) method of GK2Atends to assign clouds at higher altitude, which mainly causes slow wind speed biases, especially inthe lower atmosphere. The IR/WV intercept method of GK2A assigns clouds slightly lower in theatmospheric layers below the altitude of 400 hPa, which causes positive biases. Quality control (QC)criteria to select the most suitable GK2A AMV data for assimilation are presented based on thesequality assessments. A new QC criterion utilizing height errors within the GEOS ADAS is introducedto exclude data with slow wind speed biases and large errors. GEOS forecast accuracy is slightlyimproved after assimilating GK2A AMVs along with other conventional, radiance, and satellitewinds which include AMVs made by the Himawari-8 satellite in nearly the same observational areaof GK2A. Additionally, the present work shows that GEOS forecasts can be significantly improved,especially in the tropics and southern hemisphere after assimilating GK2A data in the absence ofHimawari-8 AMVs. This study demonstrates that GK2A AMV data is a valuable data source toenhance the robustness of GEOS ADAS.

Atmospheric Motion Vectors↗

NASA NTRS2000

Assimilation of (A) TOVS data at the NASA Goddard Data Assimilation Office

At the NASA Goddard Data Assimilation Office (DAO), a 1D variational radiance assimilation system has been developed. This system, called DAOTOVS (Tiros operational vertical sounder (TOVS)), uses (A)TOVS level 1b radiances. It has been implemented within the DAO's semi-operational system as well as within the next generation data assimilation system that uses a finite-volume dynamical core. We will show results from (A)TOVS assimilation, including stratospheric analyses and validation. We will also describe our systematic error correction scheme which is based on collocated radiosondes.

Joiner, Joanna↗

NASA NTRS2003

Temperature Data Assimilation with Salinity Corrections: Validation for the NSIPP Ocean Data Assimilation System in the Tropical Pacific Ocean, 1993-1998

The NASA Seasonal-to-Interannual Prediction Project (NSIPP) has developed an Ocean data assimilation system to initialize the quasi-isopycnal ocean model used in our experimental coupled-model forecast system. Initial tests of the system have focused on the assimilation of temperature profiles in an optimal interpolation framework. It is now recognized that correction of temperature only often introduces spurious water masses. The resulting density distribution can be statically unstable and also have a detrimental impact on the velocity distribution. Several simple schemes have been developed to try to correct these deficiencies. Here the salinity field is corrected by using a scheme which assumes that the temperature-salinity relationship of the model background is preserved during the assimilation. The scheme was first introduced for a zlevel model by Troccoli and Haines (1999). A large set of subsurface observations of salinity and temperature is used to cross-validate two data assimilation experiments run for the 6-year period 1993-1998. In these two experiments only subsurface temperature observations are used, but in one case the salinity field is also updated whenever temperature observations are available.

Troccoli, Alberto↗

NASA NTRS1995

Technical Report Series on Global Modeling and Data Assimilation. Volume 7: Proceedings of the Workshop on the GEOS-1 Five-year Assimilation

The primary objective of the three-day workshop on results from the Data Assimilation Office (DAO) five-year assimilation was to provide timely feedback from the data users concerning the strengths and weaknesses of version 1 of the Goddard Earth Observing System (GEOS-1) assimilated products. A second objective was to assess user satisfaction with the current methods of data access and retrieval. There were a total of 49 presentations, with about half (23) of the presentations from scientists from outside of Goddard. The first two days were devoted to applications of data: studies of the energy diagnostics, precipitation and diabatic heating, hydrological modeling and moisture transport, cloud forcing and validation, various aspects of intraseasonal, seasonal, and interannual variability, ocean wind stress applications, and validation of surface fluxes. The last day included talks from the National Meteorological Center (NMC), the National Center for Atmospheric Research (NCAR), the Center for Ocean-Land-Atmosphere Studies (COLA), the United States Navy, and the European Center for Medium Range Weather Forecasts (ECMWF).

Suarez, Max J.↗

NASA NTRS1998

Technical Report Series on Global Modeling and Data Assimilation: A Comparison of GEOS Assimilated Data with FIFE Observations - Volume 14

First ISLSCP Field Experiment (FIFE) observations have been used to validate the near-surface proper- ties of various versions of the Goddard Earth Observing System (GEOS) Data Assimilation System. The site- averaged FIFE data set extends from May 1987 through November 1989, allowing the investigation of several time scales, including the annual cycle, daily means and diurnal cycles. Furthermore, the development of the daytime convective planetary boundary layer is presented for several days. Monthly variations of the surface energy budget during the summer of 1988 demonstrate the affect of the prescribed surface soil wetness boundary conditions. GEOS data comes from the first frozen version of the assimilation system (GEOS-1 DAS) and two experimental versions of GEOS (v. 2.0 and 2.1) with substantially greater vertical resolution and other changes that influence the boundary layer. This report provides a baseline for future versions of the GEOS data assimilation system that will incorporate a state-of-the-art land surface parameterization. Several suggestions are proposed to improve the generality of future comparisons. These include the use of more diverse field experiment observations and an estimate of gridpoint heterogeneity from the new land surface parameterization.

Bosilovich, Michael G.↗