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Passive and Active L-Band System and Observations During the 2007 CLASIC Campaign

This article describes the upgraded PALS instrument and the characteristics of data acquired from the Cloud Land Atmospheric Interaction Campaign (CLASIC) 2007. The data acquired over lake passes were used to remove the radiometer calibration bias. The calibrated radiometer data showed significant consistency with the L-band land emission model for soil surfaces published in the literature. We observed significant temporal (days) changes of a few dB in the radar data. The change of radar backscatter appeared to correlate well with the change of in-situ soil moisture or the soil moisture data derived from the PALS dual-polarized brightness temperatures. The radar vegetation index also correlated well with the vegetation opacity estimated from the radiometer data. The preliminary analyses suggest complementary information contained in the surface emissivity and backscatter signatures for the retrieval of soil moisture and vegetation water content.

Soil moisture↗

Design and Impacts of Land-Biogenic-Atmosphere Coupling in the NASA-Unified WRF (NU-WRF) Modeling System

Land-Atmosphere coupling is typically designed and implemented independently for physical (e.g. water and energy) and chemical (e.g. biogenic emissions and surface depositions)-based models and applications. Differences in scale, data requirements, and physics thus limit the ability of Earth System models to be fully coupled in a consistent manner. In order for the physical-chemical-biological coupling to be complete, treatment of the land in terms of surface classification, condition, fluxes, and emissions must be considered simultaneously and coherently across all components. In this study, we investigate a coupling strategy for the NASA-Unified Weather Research and Forecasting (NU-WRF) model that incorporates the traditionally disparate fluxes of water and energy through NASA's LIS (Land Information System) and biogenic emissions through BEIS (Biogenic Emissions Inventory System) and MEGAN (Model of Emissions of Gases and Aerosols from Nature) into the atmosphere. In doing so, inconsistencies across model inputs and parameter data are resolved such that the emissions from a particular plant species are consistent with the heat and moisture fluxes calculated for that land cover type. In turn, the response of the atmospheric turbulence and mixing in the planetary boundary layer (PBL) acts on the identical surface type, fluxes, and emissions for each. In addition, the coupling of dust emission within the NU-WRF system is performed in order to ensure consistency and to maximize the benefit of high-resolution land representation in LIS. The impacts of those self-consistent components on' the simulation of atmospheric aerosols are then evaluated through the WRF-Chem-GOCART (Goddard Chemistry Aerosol Radiation and Transport) model. Overall, this ambitious project highlights the current difficulties and future potential of fully coupled. components. in Earth System models, and underscores the importance of the iLEAPS community in supporting improved knowledge of processes and innovative approaches for models and observations.

Tan, Qian↗

Coupled land surface/hydrologic/atmospheric models

The topics covered include the following: prototype land cover characteristics data base for the conterminous United States; surface evapotranspiration effects on cumulus convection and implications for mesoscale models; the use of complex treatment of surface hydrology and thermodynamics within a mesoscale model and some related issues; initialization of soil-water content for regional-scale atmospheric prediction models; impact of surface properties on dryline and MCS evolution; a numerical simulation of heavy precipitation over the complex topography of California; representing mesoscale fluxes induced by landscape discontinuities in global climate models; emphasizing the role of subgrid-scale heterogeneity in surface-air interaction; and problems with modeling and measuring biosphere-atmosphere exchanges of energy, water, and carbon on large scales.

Pielke, Roger↗

Subsurface Emission Effects in AMSR-E Measurements: Implications for Land Surface Microwave Emissivity Retrieval

An analysis of land surface microwave emission time series shows that the characteristic diurnal signature associated with subsurface emission in sandy deserts carry over to arid and semi-arid region worldwide. Prior work found that diurnal variation of Special Sensor Microwave/Imager (SSM/I) brightness temperatures in deserts was small relative to International Satellite Cloud Climatology Project land surface temperature (LST) variation and that the difference varied with surface type and was largest in sand sea regions. Here we find more widespread subsurface emission effects in Advanced Microwave Scanning Radiometer-EOS (AMSR-E) measurements. The AMSR-E orbit has equator crossing times near 01:30 and 13 :30 local time, resulting in sampling when near-surface temperature gradients are likely to be large and amplifying the influence of emission depth on effective emitting temperature relative to other factors. AMSR-E measurements are also temporally coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements, eliminating time lag as a source of LST uncertainty and reducing LST errors due to undetected clouds. This paper presents monthly global emissivity and emission depth index retrievals for 2003 at 11, 19, 37, and 89 GHz from AMSR-E, MODIS, and SSM/I time series data. Retrieval model fit error, stability, self-consistency, and land surface modeling results provide evidence for the validity of the subsurface emission hypothesis and the retrieval approach. An analysis of emission depth index, emissivity, precipitation, and vegetation index seasonal trends in northern and southern Africa suggests that changes in the emission depth index may be tied to changes in land surface moisture and vegetation conditions

Galantowicz, John F.↗

Interactive Computing and Processing of NASA Land Surface Observations Using Google Earth Engine

Google's Earth Engine offers a "big data" approach to processing large volumes of NASA and other remote sensing products. h\ps://earthengine.google.com/ Interfaces include a Javascript or Python-based API, useful for accessing and processing over large periods of record for Landsat and MODIS observations. Other data sets are frequently added, including weather and climate model data sets, etc. Demonstrations here focus on exploratory efforts to perform land surface change detection related to severe weather, and other disaster events.

earth engine↗

Assessment of Retrieved GMI Emissivity Over Land, Snow and Sea Ice in the GEOS System

Measurements from microwave sounders and imagers provide a valuable source of information including atmospheric temperature and water vapor in Numerical Weather Prediction (NWP) systems that assimilate these observations directly over water surfaces (oceans and other large water bodies). In a recent decadal survey, targeted observables in the Planetary Boundary Layer (PBL) were cited as a key need for future observations (NASEM, 2018). Microwave observations which sense in the PBL are currently available, however, utilizing surface-sensitive microwave observations for atmospheric data assimilation remains a challenge over land, snow and sea ice. This is in part due to the inability of surface emissivity models used by NWP data assimilation systems to simulate observations with sufficient accuracy. The GEOS-ADAS (Todling and el Akkraoui, 2018) which utilizes the Community Radiative Transfer Model (CRTM) (Han, 2006; Chen 2009) is no exception. The ECMWF system has retrieved instantaneous surface emissivity from surface-sensitive channels for SSMI/S and MHS radiance observations, and apply these estimates to the closest channels higher in frequency (Baordo and Geer 2016) in the calculation of simulated radiances. This approach currently is also being tested in the GEOS-ADAS for AMSU-A and ATMS radiances (Zhu et al. 2021). No or minimal emissivity spectral variability has been assumed in the above-mentioned studies. Recently, work by Munchak et al., 2020 (hereby referred to as M2020) provided a new database for emissivity over land, snow and sea ice retrieved from the NASA Global Precipitation Mission (GPM). Compared with Tool to Estimate Land Surface Emissivities at Microwave (TELSEM2; Wang et al., 2017), M2020 provides emissivities for more frequencies(i.e., 10.7 GHz V/H). Moreover, this database is unique in that it utilizes both active and passive data to retrieve surface emissivity and normalized radar cross section. While the emissivity values may be useful for other sensors, they are most applicable to the GPM Microwave Imager (GMI). In this work the GEOS-ADAS is modified to utilize emissivity values from Munchak et al, 2020 in place of values used by CRTM. Presently, only GMI radiances over ocean are used in the operational GEOS-ADAS. This study will focus on the GMI radiances over land, snow, and ice, as a first attempt to evaluate GMI radiances over these non-water surface types. Two cases are then presented, one with one week of observation minus background departures using the modified GEOS-ADAS, and one utilizing the original GEOS-ADAS. It should be noted that the surface emissivity models in CRTM are not state of the art and are scheduled to be replaced by the Community Surface Emissivity Module (CSEM; Chen and Weng, 2016). Simulations using default CRTM emissivity values are used merely as reference comparing against M2020, and is not a thorough comparison against other more state of the art modules such as CSEM.

GMI↗

Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models

Land surface (or "skin") temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. Here we assimilate LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) into the Noah and Catchment (CLSM) land surface models using an ensemble-based, off-line land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LST typically exhibit different mean values and variability. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation ("open loop") are comparable to each other and superior to that of ISCCP retrievals. For LST, RMSE values are 4.9 K (CLSM), 5.6 K (Noah), and 7.6 K (ISCCP), and anomaly correlation coefficients (R) are 0.62 (CLSM), 0.61 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over open loop) of up to 0.7 K in RMSE and 0.05 in anomaly R. The skill of surface turbulent flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.

Reichle, Rolf H.↗

IN11B-1621: Quantifying How Climate Affects Vegetation in the Amazon Rainforest

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

global warming↗

Selection and Assessment of Predictions of the Mars Pathfinder Landing Site

The successful landing of the Mars Pathfinder spacecraft on Mars allows the review of the process of selecting the landing site and assessing predictions made for the site based on Viking and Earth-based data. Selection of the landing site for Mars Pathfinder was a two-phase process. The first phase took place from October 1993 to June 1994 and involved: initial identification of engineering constraints, definition of environmental conditions at the site for spacecraft design, and evaluation of the scientific potential of different landing sites. This phase culminated with the first "Mars Pathfinder Landing Site Workshop", held at the Lunar and Planetary Institute in Houston, Texas on April 18-19, 1994, in which suggested approaches and landing sites were solicited from the entire scientific community. A preliminary site was selected by the project for design purposes in June 1994. The second phase took place from July 1994 to March 1996 and involved: developing criteria for evaluating site safety using images and remote sensing data, testing of the spacecraft and landing subsystems (with design improvements) to establish quantitative engineering constraints on landing site characteristics, evaluating all potential landing sites on Mars, and certification of the site by the project. This phase included a second open workshop, "Mars Pathfinder Landing Site Workshop II: Characteristics of the Ares Vallis Region and Field Trips in the Channeled Scabland, Washington" held in Spokane and Moses Lake September 24-30, 1995 and formal acceptance of the site by NASA Headquarters. Engineering constraints on Pathfinder landing sites were developed from the initial design of the spacecraft and the entry, descent and landing scenario. The site must be within 5 degrees of the subsolar latitude at the time of landing (15N for maximum solar power and flexible communications with Earth. It also must be below 0 km elevation to enable enough time for the parachute to bring the lander to the proper terminal velocity for landing. The entire landing ellipse, which is 70 km by 200 km due to navigational, ephemeris and atmospheric uncertainties, must be free of steep slopes, scarps and obvious hazards in Viking orbiter images, have acceptable radar reflectivity, moderate rock abundances and have little or no dust. Scientific considerations of the Mars Pathfinder payload and mission indicate that analyses of "grab bag" samples at the mouths of outflow channels can offer a first order assessment of a variety of rock types on Mars. Highland sites offer the advantage of in situ analysis of ancient rocks on Mars that record crustal differentiation and the nature of the early environment. Dark gray sites offer the potential of analyzing unweathered and unoxidized materials. Following a general assessment of the safety of different sites, a preliminary selection of a "grab bag" site was made. This site, Ares Vallis, is near the mouth of an outflow channel that may contain ancient Noachian terrain, Hesperian ridged plains, and reworked channel materials. All potential landing sites on Mars that met basic safety criteria were analyzed in detail. Sites (100 by 200 km target ellipses) were considered safe if they were below 0 km elevation, were free of obvious hazards (high relief surface features) in high-resolution (< 50 m/pixel) Viking orbiter images and had acceptable reflectivity and roughness at radar wavelengths, high thermal inertia, moderate rock abundance, low red to violet ratio, and low albedo. Only 4 sites on Mars met all the above criteria, which included 1995 opposition 3.5 cm delay-Doppler radar data. Complete data were evaluated for 7 sites and the Viking landing sites for comparison for all the above criteria as well as crater abundance, hill and mesa abundance, slopes over meter to kilometer scales, low altitude winds (from global circulation models and slopes), the size-frequency distribution of large rocks, as well as rover trafficability and science potential. Discussion of potential hazards at Ares Vallis using a variety of data sets (including radar) at a second open workshop, indicated this site cannot be shown to be any more hazardous than the Viking landing sites. Field trips to the Channeled Scabland and the Ephrata Fan, analogs for Ares Vallis and the landing site, respectively, provided valuable insight into possible geologic processes and potential surface characteristics. Three sites met all the data requirements and safety criteria for landing Pathfinder. Ares Vallis was selected by the project because it appeared acceptably safe (although it appeared to have greater rock abundances than other sites, its elevation was likely the best known) and offered the prospect of analyzing a variety of rock types expected to be deposited by catastrophic floods, which would enable addressing first-order scientific questions such as differentiation of the crust, the development of weathering products, and the nature of the early martian environment and its subsequent evolution. The selection was reviewed by an external board at a number of meetings and accepted, and the site was approved by NASA Headquarters. Data gathered by the Pathfinder lander' and rover provides the opportunity to test the predictions made for the site in the selection process based on remote observations from Earth, orbit, and the surface. The discussion below is taken from Golombek et al. to which the reader is referred for a more complete discussion and a complete list of references, which are omitted here for brevity. Many characteristics of the landing site are consistent with its being shaped and deposited by the Ares and Tiu catastrophic floods. The rocky surface is consistent a depositional plain comprising semi-rounded pebbles, cobbles and tabular boulders (some of which appear imbricated and/or inclined in the direction of flow) that appear similar to depositional plains in terrestrial catastrophic floods. The Twin Peaks appear to be streamlined hills in lander images, which is consistent with interpretations of larger hills in Viking orbiter images of the region that suggest the lander is on the flank of a broad, gentle ridge trending northeast from Twin Peaks. This ridge, which is the rise to the north of the lander, is aligned in the downstream direction from the Ares and Tiu Valles floods, and may be a debris tail deposited in the wake of the Twin Peaks. Channels visible throughout the scene may be a result of late stage drainage. As predicted by delay-Doppler radar measurements and tracking results, the average elevation of the center of the site was about the same as Viking Lander I relative to the 6.1 mbar geoid. The Doppler tracking and two-way ranging estimate for the elevation of the spacecraft is only 45 in lower than the Viking I Lander and within 100 in of that expected, which is within the uncertainties of the measurements. After landing, surface pressures and winds (5-10 m/s) were found to be similar to expectations based on Viking data, although temperatures were about 10 K warmer. The temperature profile below 50 km was also roughly 20 K warmer. As a result, predicted densities were 5% higher near the surface and up to 40% lower at 50 km but within the entry, descent and landing design margins. The populations of craters and small hills and the slopes of the hills measured in high-resolution (38 m/pixel) Viking orbiter images and the radar derived slopes of the landing site are all consistent with observations of these properties in the lander images. A rocky surface was expected from Viking Infra-Red Thermal Mapper (IRTM) observations and comparisons with the Viking landing sites. The observed cumulative fraction of area covered by rocks with diameters greater than 3 cm and heights greater than 0.5 in (potentially hazardous to landing) at Ares is similar to that predicted by IRTM observations and models of Viking lander and Earth analog rock size-frequency distributions. The IRTM prediction postulated an effective thermal inertia of 30 (10(exp -3) cgs units - cal/cubic cm/s(exp 0.5)/K) for the rock population, but we obtain a slightly different effective thermal inertia for the actual rock population. The validity of interpretations of radar echoes prior to landing are supported by a simple radar echo model, an estimate of the reflectivity of the soil from its bulk density, and the fraction of area covered by rocks. In the calculations, the soil produces the quasi-specular echo and the rocks produce the diffuse echo. The derived quasispecular cross section is comparable to the cross-sections and reflectivities reported for 3.5-cm wavelength observations. The model yields a diffuse echo that is modestly larger than the polarized diffuse echo reported for 3.5-cm wavelength observations. At 12.5-cm wavelength, similar rock populations at Ares and the Viking I site were expected because the diffuse echoes are comparable, but the large normal reflectivities suggests that bulk densities of the soils at depth are greater than those at the surface. We also obtain a fine-component inertia near 8.4 which agrees with the fine-component inertia of 8.7 (in 10(exp -3) cgs units) estimated from thermal observations from orbit by the IRTM; for this estimate, we used a bulk thermal inertia of 10.4 for the landing site, an effective thermal inertia near 40 (10(exp -3) cgs units) for the rock population, and a graphical representation of Kieffer's model. Color and albedo data for Ares suggested surfaces of materials at Ares Vallis would be relatively dust free or unweathered prior to landing compared with the materials at the Viking landing sites. This suggestion is supported by the abundance of relatively dark-gray rocks at Ares and their relative rarity at the Viking landing sites, where rocks are commonly coated with bright red dust. Finally, the 40 km long Ephrata Fan of the Channeled Scabland in Washington state, which was deposited where c

Golombek, M. P.↗

Multispectral satellite observations for arid land studies

Multispectral satellite data when properly calibrated and standardized can be used synergistically for a quantitative analysis of processes and surface characteristics, and for quantifying land surface change. Relationships among multispectral satellite data (visible reflectance, surface temperature and polarization difference of microwave emission at 37 GHz frequency) have been used to develop hypotheses concerning the relative sensitivity of these data to varied land surface characteristics, which needs to be verified by field observations. Radiative transfer models have also been developed to understand these multispectral data. Interannual variations of visible reflectance and polarization difference for the period 1982-1986 over the Sahel and the Sudan zones of Africa show a lagged response with respect to the rainfall deficit during recovery from drought, which needs to be understood in terms of biophysical parameters.

Choudhury, Bhaskar J.↗

The Soil Moisture Active and Passive (SMAP) Mission

The Soil Moisture Active and Passive (SMAP) Mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council s Decadal Survey. SMAP will make global measurements of the moisture present at Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy and carbon transfers between land and atmosphere. Soil moisture measurements are also of great importance in assessing flooding and monitoring drought. SMAP observations can help mitigate these natural hazards, resulting in potentially great economic and social benefits. SMAP soil moisture and freeze/thaw timing observations will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept would utilize an L-band radar and radiometer. These instruments will share a rotating 6-meter mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. The SMAP instruments provide direct measurements of surface conditions. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and estimates of land surface-atmosphere exchanges of water, energy and carbon. SMAP is scheduled for a 2014 launch date

Entekhabi, Dara↗

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↗

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗

Development of a New Data Tool for Computing Launch and Landing Availability with Respect to Surface Weather

The Marshall Space Flight Center Natural Environments Branch has a long history of expertise in the modeling and computation of statistical launch availabilities with respect to weather conditions. Their existing data analysis product, the Atmospheric Parametric Risk Assessment (APRA) tool, computes launch availability given an input set of vehicle hardware and/or operational weather constraints by calculating the climatological probability of exceeding the specified constraint limits, APRA has been used extensively to provide the Space Shuttle program the ability to estimate impacts that various proposed design modifications would have to overall launch availability. The model accounts for both seasonal and diurnal variability at a single geographic location and provides output probabilities for a single arbitrary launch attempt. Recently, the Shuttle program has shown interest in having additional capabilities added to the APRA model, including analysis of humidity parameters, inclusion of landing site weather to produce landing availability, and concurrent analysis of multiple sites, to assist in operational landing site selection. In addition, the Constellation program has also expressed interest in the APRA tool, and has requested several additional capabilities to address some Constellation-specific issues, both in the specification and verification of design requirements and in the development of operations concepts. The combined scope of the requested capability enhancements suggests an evolution of the model beyond a simple revision process. Development has begun for a new data analysis tool that will satisfy the requests of both programs. This new tool, Probabilities of Atmospheric Conditions and Environmental Risk (PACER), will provide greater flexibility and significantly enhanced functionality compared to the currently existing tool.

Burns, K. Lee↗

Simulation of the Onset of the Southeast Asian Monsoon during 1997 and 1998: The Impact of Surface Processes

The onset of the southeast Asian monsoon during 1997 and 1998 was simulated by coupling a mesoscale atmospheric model (MM5) and a detailed, land surface model, PLACE (the Parameterization for Land-Atmosphere-Cloud Exchange). The rainfall results from the simulations were compared with observed satellite data from the TRMM (Tropical Rainfall Measuring Mission) TMI (TRMM Microwave Imager) and GPCP (Global Precipitation Climatology Project). The control simulation with the PLACE land surface model and variable sea surface temperature captured the basic signatures of the monsoon onset processes and associated rainfall statistics. Sensitivity tests indicated that simulations were sigmficantly improved by including the PLACE land surface model. The mechanism by which the land surface processes affect the moisture transport and the convection during the onset of the southeast Asian monsoon were analyzed. The results indicated that land surface processes played an important role in modifying the low-level wind field over two major branches of the circulation: the southwest low-level flow over the Indo-china peninsula and the northern, cold frontal intrusion from southern China. The surface sensible and latent heat fluxes modified the low-level temperature distribution and gradient, and therefore the low-level wind due to the thermal wind effect. The more realistic forcing of the sensible and latent heat fluxes from the detailed, land surface model improved the low-level wind simulation apd associated moisture transport and convection.

Wang, Yansen↗

Simulation of the Onset of the Southeast Asian Monsoon during 1997 and 1998: The Impact of Surface Processes

The onset of the southeast Asian monsoon during 1997 and 1998 was simulated by coupling a mesoscale atmospheric model (MM5) and a detailed, land surface model, PLACE (the Parameterization for Land-Atmosphere-Cloud Exchange). The rainfall results from the simulations were compared with observed satellite data from the TRMM (Tropical Rainfall Measuring Mission) TMI (TRMM Microwave Imager) and GPCP (Global Precipitation Climatology Project). The control simulation with the PLACE land surface model and variable sea surface temperature captured the basic signatures of the monsoon onset processes and associated rainfall statistics. Sensitivity tests indicated that simulations were significantly improved by including the PLACE land surface model. The mechanism by which the land surface processes affect the moisture transport and the convection during the onset of the southeast Asian monsoon were analyzed. The results indicated that land surface processes played an important role in modifying the low-level wind field over two major branches of the circulation: the southwest low-level flow over the Indo-China peninsula and the northern, cold frontal intrusion from southern China. The surface sensible and latent heat fluxes modified the low-level temperature distribution and merit, and therefore the low-level wind due to the thermal wind effect. The more realistic forcing of the sensible and latent heat fluxes from the detailed, land surface model improved the low-level wind simulation and associated moisture transport and convection.

Wang, Yansen↗

Improving the Representation of Land Surface Processes Using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here, we emphasize the implementation of tools and approaches to overcome challenges related to land DA to constrain carbon and water cycling. In particular, we discuss the implementation of adaptive inflation to modify ensemble spread in response to time-varying networks of gridded observations. We also discuss methods to generate ensemble spread through boundary condition (meteorology) forcing that can be applied to site-level applications. Next, we describe the application of vertical localization upon surface soil moisture observations, and forward operators specifically designed for the assimilation of snow and solar-induced fluorescence observations. Finally, we discuss the potential benefit of a quantile conserving filter used to update bounded quantities (state or parameter values).

Brett Raczka↗