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NASA NTRSDate not supplied

Version 8 of the SMAP Level-4 Soil Moisture Data Assimilation Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The product is based on the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observations-based precipitation forcing. This presentation discusses the improvements in the forthcoming Version 8 of L4_SM, including updates in the precipitation forcing, the Catchment model parameters, and the L-band microwave radiative transfer model (mwRTM). The precipitation observations used in L4_SM Version 8 outside of North America and the high latitudes are from the latest (Version 7) NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) products. Moreover, occasionally excessive precipitation rates in earlier versions of L4_SM along certain longitudes in North America were eliminated by a bug fix in the precipitation corrections algorithm. The Catchment model in L4_SM Version 8 uses climatological snow albedo values based on observations from the Moderate Resolution Imaging Spectroradiometer, replacing the look-up table parameterization of earlier versions. Additionally, corrected soil parameters were implemented for a small region in Argentina that had erroneously been classified as peat because of an error in the ancillary soil data. Finally, the mwRTM in L4_SM Version 8 uses the Mironov soil mixing approach and updated values of the L-band scattering albedo, soil roughness, and vegetation opacity climatology obtained from the latest (Release 19) SMAP Level-2 dual-channel soil moisture retrieval product. During the development of L4_SM Version 8, the change in the mwRTM parameterization resulted in a reduced unbiased RMSE of surface soil moisture when verified against in situ measurements. It also reduced the standard deviation of the Tb observation-minus-forecast residuals by ~0.15 K, highlighting the importance of the mwRTM for successful data assimilation. The bug fixes in the precipitation corrections algorithm and the Catchment model soil parameters in the Argentina region result in locally large improvements of the simulated land surface states. In summary, the ongoing refinements of the L4_SM product continue to improve its science quality and performance for global soil moisture monitoring

Rolf Reichle↗
NASA NTRS2015

Major Upgrades to the AIRS Version-6 Water Vapor Profile Methodology

This research is a continuation of part of what was shown at the last AIRS Science Team Meeting and the AIRS 2015 NetMeeting. AIRS Version 6 was finalized in late 2012 and is now operational. Version 6 contained many significant improvements in retrieval methodology compared to Version 5. Version 6 retrieval methodology used for the water vapor profile q(p) and ozone profile O3(p) retrievals is basically unchanged from Version 5, or even from Version 4. Subsequent research has made significant improvements in both water vapor and O3 profiles compared to Version 6.

Version-6↗
NASA NTRS1982

Algorithms for software development version control and change detection

Simple computer algorithms for processing source program and text data files in order to extract change detection, version control, version history, and current status information are described. These algorithms presuppose that it is possible to attach to each record of the source files a 6-character code, placed within delimiters that will cause the compiler, or other using program, to ignore this code field. The code contains a 2-character code for a character-by-character position-sensitive checksum of the record, another for the record number in the file, and a third for the data on which the encoding took place. Once the source file has been thus encoded, it is possible to detect the following transactions on the file since the most recent version coding; (1) addition of new records (having no version code), (2) modification of existing records, (3) deletion of a number of records, (4) movement and/or duplication of existing records, and (5) modification and duplication of records. In addition, it is possible to extract a version history of the number of records created or modified by date. A special file listing program is described which prints the file records without showing the version codes, but places a "change bar" at the right margin whenever a change is detected. The program also provides a list of changed pages and a version history.

Tausworthe, R. C.↗
NASA NTRS1985

CARE 3, Version 4 enhancements

The enhancements and error corrections to CARE III Version 4 are listed. All changes to Version 4 with the exception of the internal redundancy model were implemented in Version 5. Version 4 is the first public release version for execution on the CDC Cyber 170 series computers. Version 5 is the second release version and it is written in ANSI standard FORTRAN 77 for execution on the DEC VAX 11/700 series computers and many others.

Bryant, L. A.↗
NASA NTRS2009

The GEOS-Chemistry-Climate Model (CCM), Versions 1,2 and 3

This paper examines evolution of key ozone and climate aspects in our range of Goddard Earth Observing System Models, Versions 1-3. Version 1 (Pawson et al., 2008; )GR) used GSFC stratospheric ozone chemistry in GEOS-4 GCM. A chronic high bias in polar ozone at low chlorine loading persisted into Version 2, which used the GEQS-5 GCM and the same chemistry. This is much improved in Version 3, which uses the GMI-COMBO stratosphere-troposphere chemistry. A dynamical problem with Version 1, the overactive nature of the Antarctic polar vortex, is corrected in Versions 2 and 3, when GEQS-5 replaces GEO5-4. Other changes between the various model versions will be documented.

Pawson, Steven↗
NASA NTRS2011

An Accuracy Assessment of the CALIOP/CALIPSO Version 2/Version 3 Daytime Aerosol Extinction Product Based on a Detailed Multi-Sensor, Multi-Platform Case Study

The Cloud Aerosol LIdar with Orthogonal Polarization (CALIOP), on board the CALIPSO platform, has measured profiles of total attenuated backscatter coefficient (level 1 products) since June 2006. CALIOP s level 2 products, such as the aerosol backscatter and extinction coefficient profiles, are retrieved using a complex succession of automated algorithms. The goal of this study is to help identify potential shortcomings in the CALIOP version 2 level 2 aerosol extinction product and to illustrate some of the motivation for the changes that have been introduced in the next version of CALIOP data (version 3, released in June 2010). To help illustrate the potential factors contributing to the uncertainty of the CALIOP aerosol extinction retrieval, we focus on a one-day, multi-instrument, multiplatform comparison study during the CALIPSO and Twilight Zone (CATZ) validation campaign on 4 August 2007. On that day, we observe a consistency in the Aerosol Optical Depth (AOD) values recorded by four different instruments (i.e. spaceborne MODerate Imaging Spectroradiometer, MODIS: 0.67 and POLarization and Directionality of Earth s Reflectances, POLDER: 0.58, airborne High Spectral Resolution Lidar, HSRL: 0.52 and ground-based AErosol RObotic NETwork, AERONET: 0.48 to 0.73) while CALIOP AOD is a factor of two lower (0.32 at 532 nm). This case study illustrates the following potential sources of uncertainty in the CALIOP AOD: (i) CALIOP s low signal-to-noise ratio (SNR) leading to the misclassification and/or lack of aerosol layer identification, especially close to the Earth s surface; (ii) the cloud contamination of CALIOP version 2 aerosol backscatter and extinction profiles; (iii) potentially erroneous assumptions of the aerosol extinction-to-backscatter ratio (Sa) used in CALIOP s extinction retrievals; and (iv) calibration coefficient biases in the CALIOP daytime attenuated backscatter coefficient profiles. The use of version 3 CALIOP extinction retrieval for our case study seems to partially fix factor (i) although the aerosol retrieved by CALIOP is still somewhat lower than the profile measured by HSRL; the cloud contamination (ii) appears to be corrected; no particular change is apparent in the observation-based CALIOP Sa value (iii). Our case study also showed very little difference in version 2 and version 3 CALIOP attenuated backscatter coefficient profiles, illustrating a minor change in the calibration scheme (iv).

Kacenelenbogen, M.↗
NASA NTRS2018

A Quick Summary of IMERG Versions and Features

This talk will summarize the shifts in IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)) from Version 03 to 04 in early Spring 2016, and to Version 05 in late Summer 2017. For example, Version 04 replaced approximate pre-launch calibrations with GPM Core Observatory-based calibrations, while Version 05 introduced improved estimates for the primary GPM instrument products (DPR, GMI, and Combined Instrument). In Version 04 the IR estimates were routinely calibrated to the passive microwave estimates. As analysis showed that the Combined Instrument estimates (the IMERG calibration standard) tend to be biased high over land and low over ocean at higher latitudes, in Version 04 we climatologically calibrated IMERG to the Global Precipitation Climatology Project (GPCP) monthly Satellite-Gauge product, except in low- and mid-latitude ocean regions. This calibration leaves the relative time series intact, and only adjusts the mean of the entire series. In Version 05 the primary GPM instrument products have reduced biases, but calibration to GPCP continues to be necessary to achieve the most realistic estimates. Finally, retrospective processing back into the TRMM (Tropical Rainfall Measuring Mission) era is expected in early 2018, after which the legacy TMPA (TRMM Multi-satellite Precipitation Analysis) dataset will be retired.

Huffman, George J.↗
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 NTRS1990

Analysis of faults in an N-version software experiment

The authors have conducted a large-scale experiment in N-version programming. A total of 27 versions of a program were prepared independently from the same specification at two universities. The results of executing the versions revealed that the versions were individually extremely reliable but that the number of input cases in which more than one failed was substantially more than would be expected if they were statistically independent. After the versions had been executed, the failures of each version were examined and the associated faults located. The analysis showed that in some cases the programmers made equivalent logical errors, indicating that some parts of the problem were simply more difficult than others. The authors also found cases in which apparently different logical errors yielded faults that caused statistically correlated failures, indicating that there are special cases in the input space that present difficulty in various parts of the solution. A formal model is presented to explain this phenomenon. It appears that minor differences in the software development environment would not have a major impact in reducing the incidence of faults that cause correlated failures.

Brilliant, Susan S.↗
NASA NTRS1994

HST archive primer, version 4.1

This version of the HST Archive Primer provides the basic information a user needs to know to access the HST archive via StarView the new user interface to the archive. Using StarView, users can search for observations interest, find calibration reference files, and retrieve data from the archive. Both the terminal version of StarView and the X-windows version feature a name resolver which simplifies searches of the HST archive based on target name. In addition, the X-windows version of StarView allows preview of all public HST data; compressed versions of public images are displayed via SAOIMAGE, while spectra are plotted using the public plotting package, XMGR. Finally, the version of StarView described here features screens designed for observers preparing Cycle 5 HST proposals.

Fruchter, A.↗
NASA NTRS2001

A Performance Comparison for Two Versions of the Vulcan Photometer

Analysis of the images produced by the first version (V1) of the Vulcan photometer indicated that two major sources of noise were sky brightness and image motion. To reduce the effect of the sky brightness, a second version (V2) with a longer focal length and a larger format detector was developed and tested. The first version consisted of 15-centimeter (cm) focal length, F/1.5 Aerojet Delft reconnaissance lens, and a 2048 x 2048 format front-illuminated charged coupled device (CCD) with 9 microns micropixels (Mpixels). The second version used a 30-cm focal length, F/2.5 Kodak AeroEktar lens, and a 4096 x 4096 format CCD with 9 micro pixels. Both have a 49-square-degree field of view (FOV) but the area of the sky subtended by each pixel in the V2 version is one-fourth that of the V1 version. This modification substantially reduces the shot noise due to the sky background and allows fainter stars to be monitored for planetary transits. To remove the data gap and consequent signal-level change caused by flipping the photometer around the declination axis and to reduce image movement on the detector, several other modifications were incorporated. These include modifying the mount and stiffening the photometer and autoguider structures to reduce flexure. This paper compares the performance characteristics of each photometer and discusses tests to identify sources of systematic noise.

Borucki, W. J.↗
NASA NTRS2010

AutoGen Version 5.0

Version 5.0 of the AutoGen software has been released. Previous versions, variously denoted Autogen and autogen, were reported in two articles: Automated Sequence Generation Process and Software (NPO-30746), Software Tech Briefs (Special Supplement to NASA Tech Briefs), September 2007, page 30, and Autogen Version 2.0 (NPO- 41501), NASA Tech Briefs, Vol. 31, No. 10 (October 2007), page 58. To recapitulate: AutoGen (now signifying automatic sequence generation ) automates the generation of sequences of commands in a standard format for uplink to spacecraft. AutoGen requires fewer workers than are needed for older manual sequence-generation processes, and greatly reduces sequence-generation times. The sequences are embodied in spacecraft activity sequence files (SASFs). AutoGen automates generation of SASFs by use of another previously reported program called APGEN. AutoGen encodes knowledge of different mission phases and of how the resultant commands must differ among the phases. AutoGen also provides means for customizing sequences through use of configuration files. The approach followed in developing AutoGen has involved encoding the behaviors of a system into a model and encoding algorithms for context-sensitive customizations of the modeled behaviors. This version of AutoGen addressed the MRO (Mars Reconnaissance Orbiter) primary science phase (PSP) mission phase. On previous Mars missions this phase has more commonly been referred to as mapping phase. This version addressed the unique aspects of sequencing orbital operations and specifically the mission specific adaptation of orbital operations for MRO. This version also includes capabilities for MRO s role in Mars relay support for UHF relay communications with the MER rovers and the Phoenix lander.

Gladden, Roy E.↗
NASA NTRS2011

Improved Surface and Tropospheric Temperatures Determined Using Only Shortwave Channels: The AIRS Science Team Version-6 Retrieval Algorithm

The Goddard DISC has generated products derived from AIRS/AMSU-A observations, starting from September 2002 when the AIRS instrument became stable, using the AIRS Science Team Version-5 retrieval algorithm. The AIRS Science Team Version-6 retrieval algorithm will be finalized in September 2011. This paper describes some of the significant improvements contained in the Version-6 retrieval algorithm, compared to that used in Version-5, with an emphasis on the improvement of atmospheric temperature profiles, ocean and land surface skin temperatures, and ocean and land surface spectral emissivities. AIRS contains 2378 spectral channels covering portions of the spectral region 650 cm(sup -1) (15.38 micrometers) - 2665 cm(sup -1) (3.752 micrometers). These spectral regions contain significant absorption features from two CO2 absorption bands, the 15 micrometers (longwave) CO2 band, and the 4.3 micrometers (shortwave) CO2 absorption band. There are also two atmospheric window regions, the 12 micrometer - 8 micrometer (longwave) window, and the 4.17 micrometer - 3.75 micrometer (shortwave) window. Historically, determination of surface and atmospheric temperatures from satellite observations was performed using primarily observations in the longwave window and CO2 absorption regions. According to cloud clearing theory, more accurate soundings of both surface skin and atmospheric temperatures can be obtained under partial cloud cover conditions if one uses observations in longwave channels to determine coefficients which generate cloud cleared radiances R(sup ^)(sub i) for all channels, and uses R(sup ^)(sub i) only from shortwave channels in the determination of surface and atmospheric temperatures. This procedure is now being used in the AIRS Version-6 Retrieval Algorithm. Results are presented for both daytime and nighttime conditions showing improved Version-6 surface and atmospheric soundings under partial cloud cover.

Susskind, Joel↗
NASA NTRS2012

Significant Advances in the AIRS Science Team Version-6 Retrieval Algorithm

AIRS/AMSU is the state of the art infrared and microwave atmospheric sounding system flying aboard EOS Aqua. The Goddard DISC has analyzed AIRS/AMSU observations, covering the period September 2002 until the present, using the AIRS Science Team Version-S retrieval algorithm. These products have been used by many researchers to make significant advances in both climate and weather applications. The AIRS Science Team Version-6 Retrieval, which will become operation in mid-20l2, contains many significant theoretical and practical improvements compared to Version-5 which should further enhance the utility of AIRS products for both climate and weather applications. In particular, major changes have been made with regard to the algOrithms used to 1) derive surface skin temperature and surface spectral emissivity; 2) generate the initial state used to start the retrieval procedure; 3) compute Outgoing Longwave Radiation; and 4) determine Quality Control. This paper will describe these advances found in the AIRS Version-6 retrieval algorithm and demonstrate the improvement of AIRS Version-6 products compared to those obtained using Version-5,

Susskind, Joel↗
NASA NTRS2016

Soil Moisture Active Passive Mission L4_SM Data Product Assessment (Version 2 Validated Release)

During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public Version 2 validated release scheduled for 29 April 2016. The assessment of the Version 2 L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to up-scaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the wide geographic range of the sparse network sites, and the global assessment of the assimilation diagnostics, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 2 validation and supports the validated release of the data. An analysis of the time average surface and root zone soil moisture shows that the global pattern of arid and humid regions are captured by the L4_SM estimates. Results from the core validation site comparisons indicate that "Version 2" of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the ubRMSE: the RMSE (Root Mean Square Error) after removal of the long-term mean difference. The overall ubRMSE of the 3-hourly L4_SM surface soil moisture at the 9 km scale is 0.035 cubic meters per cubic meter requirement. The corresponding ubRMSE for L4_SM root zone soil moisture is 0.024 cubic meters per cubic meter requirement. Both of these metrics are comfortably below the 0.04 cubic meters per cubic meter requirement. The L4_SM estimates are an improvement over estimates from a model-only SMAP Nature Run version 4 (NRv4), which demonstrates the beneficial impact of the SMAP brightness temperature data. L4_SM surface soil moisture estimates are consistently more skillful than NRv4 estimates, although not by a statistically significant margin. The lack of statistical significance is not surprising given the limited data record available to date. Root zone soil moisture estimates from L4_SM and NRv4 have similar skill. Results from comparisons of the L4_SM product to in situ measurements from nearly 400 sparse network sites corroborate the core validation site results. The instantaneous soil moisture and soil temperature analysis increments are within a reasonable range and result in spatially smooth soil moisture analyses. The O-F residuals exhibit only small biases on the order of 1-3 degrees Kelvin between the (re-scaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is largely unbiased. The spatially averaged time series standard deviation of the O-F residuals is 5.9 degrees Kelvin, which reduces to 4.0 degrees Kelvin for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Averaged globally, the time series standard deviation of the normalized O-F residuals is close to unity, which would suggest that the magnitude of the modeled errors approximately reflects that of the actual errors. The assessment report also notes several limitations of the "Version 2" L4_SM data product and science algorithm calibration that will be addressed in future releases. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that the L4_SM assimilation algorithm either over- or under-estimates the actual errors that are present in the system. Planned improvements include revised land model parameters, revised error parameters for the land model and the assimilated SMAP observations, and revised surface meteorological forcing data for the operational period and underlying climatological data. Moreover, a refined analysis of the impact of SMAP observations will be facilitated by the construction of additional variants of the model-only reference data. Nevertheless, the “Version 2” validated release of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.

SMAP L4_SM↗
NASA NTRS2018

TRMM Version 8 Reprocessing Improvements and Incorporation into the GPM Data Suite

The National Aeronautics and Space Administration (NASA) has always included data reprocessing as a major component of every science mission. A final reprocessing is typically a part of mission closeout (known as phase F).The Tropical Rainfall Measuring Mission (TRMM) is currently in phase F, and NASA is preparing for the last reprocessing of all the TRMM precipitation data as part of the closeout. This reprocessing includes improvements in calibration of both the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). An initial step in the version 8 reprocessing is the improvement of geolocation. The PR calibration is being updated by the Japan Aerospace Exploration Agency (JAXA) using data collected as part of the calibration of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory. JAXA undertook a major effort to ensure TRMM PR and GPM Ku-band calibration is consistent. A major component of the TRMM version 8 reprocessing is to create consistent retrievals with the GPM version 05 (V05) retrievals. To this end, the TRMM version 8 reprocessing uses retrieval algorithms based on the GPM V05 algorithms. This approach ensures consistent retrievals from December 1997 (the beginning of TRMM) through the current ongoing GPM retrievals. An outcome of this reprocessing is the incorporation of TRMM data products into the GPM data suite. Incorporation also means that GPM file naming conventions and reprocessed TRMM data carry the V05 data product version. This paper describes the TRMM version 8 reprocessing, focusing on the improvements in TMI level 1 products.

Stocker, E. F.↗
NASA NTRS2018

CALIPSO Lidar Calibration at 532 nm: Version 4 Nighttime Algorithm

Data products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were recently updated following the implementation of new (version 4) calibration algorithms for all of the level 1 attenuated backscatter measurements. In this work we present the motivation for and the implementation of the version 4 nighttime 532 nm parallel channel calibration. The nighttime 532 nm calibration is the most fundamental calibration of CALIOP data, since all of CALIOP’s other radiometric calibration procedures – i.e., the 532 nm daytime calibration and the 1064 nm calibrations during both nighttime and daytime – depend either directly or indirectly on the 532 nm nighttime calibration. The accuracy of the 532 nm nighttime calibration has been significantly improved by raising the molecular normalization altitude from 30-34 km to 36-39 km to substantially reduce stratospheric aerosol contamination. Due to the greatly reduced molecular number density and consequently reduced signal-to-noise ratio (SNR) at these higher altitudes, the signal is now averaged over a larger number of samples using data from multiple adjacent granules. As well, an enhanced strategy for filtering the radiation-induced noise from high energy particles was adopted. Further, the meteorological model used in the earlier versions has been replaced by the improved MERRA-2 model. An aerosol scattering ratio of 1.01 ± 0.01 is now explicitly used for the calibration altitude. These modifications lead to globally revised calibration coefficients which are, on average, 2-3% lower than in previous data releases. Further, the new calibration procedure is shown to eliminate biases at high altitudes that were present in earlier versions and consequently leads to an improved representation of stratospheric aerosols. Validation results using airborne lidar measurements are also presented. Biases relative to collocated measurements acquired by the Langley Research Center (LaRC) airborne high spectral resolution lidar (HSRL) are reduced from 3.6% ± 2.2% in the version 3 data set to 1.6% ± 2.4 % in the version 4 release.

Jayanta Kar↗
NASA NTRS2019

Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially-distributed ensemble Kalman filter. Version 4 of the L4_SM modeling system includes a reduction in the upward recharge of surface soil moisture from below under non-equilibrium conditions, resulting in reduced bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation-minus-forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias-adjusted RMSE in Version 4 is 0.039 m(exp 3) m(exp -3) for surface and 0.026 m(exp 3) m(exp -3) for root-zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near-global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01-0.02 m(exp 3) m(exp -3)) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm year (exp -1)) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

RMSE↗