Search NASA⌕ Search

SEARCH · Search NASA

Results for “climatology data products”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Visualization and Analysis of Multi-scale Land Surface Products via Giovanni Portals

Large volumes of MODIS land data products at multiple spatial resolutions have been integrated into the Giovanni online analysis system to support studies on land cover and land use changes,focused on the Northern Eurasia and Monsoon Asia regions through the LCLUC program. Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), providing a simple and intuitive way to visualize, analyze, and access Earth science remotely-sensed and modeled data.Customized Giovanni Web portals (Giovanni-NEESPI andGiovanni-MAIRS) have been created to integrate land, atmospheric,cryospheric, and societal products, enabling researchers to do quick exploration and basic analyses of land surface changes, and their relationships to climate, at global and regional scales. This presentation shows a sample Giovanni portal page, lists selected data products in the system, and illustrates potential analyses with imagesand time-series at global and regional scales, focusing on climatology and anomaly analysis. More information is available at the GES DISCMAIRS data support project portal: http:disc.sci.gsfc.nasa.govmairs.

Shen, Suhung↗
NASA NTRS2001

Validation of Surface Bio-Optical Properties in the Gulf of Maine as a Means for Improving Satellite Primary Production Estimates

One of the greatest challenges in providing sea-truth data for various ocean color sensors is climatology. This is particularly true in the Gulf of Maine since it is cloudy and foggy more than it is clear; the climatology shows on average, about one in four to five days has clear skies with clear days slightly more frequent in the late summer and early fall. Our strategy has been to use a ship of opportunity where one has choice of the sampling days. This provides much better flexibility to sample during clear periods with good satellite coverage. Our Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) contract has been to use the M/S Scotia Prince ferry as a ship of opportunity, running between Portland, Maine and Yarmouth, NS. Measurements include continuous, surface, along-track fluorescence, two independent measures of backscattering, total light scattering, absorption, beam attenuation, above-water remote sensing reflectance, calcite-dependent light scattering, temperature, and salinity. Expendable bathythermography (XBT) drops allow acquisition of vertical temperature information, useful for defining isopycnal slope, which affects primary production. These data are comparable to a previous program from early 1982, where a ship of opportunity program (SOOP) was run on the truck ferry, M/V Marine Evangeline, which ran along the same transect. These surface data were combined with satellite-derived sea surface temperature fields to examine the Maine coastal current. Unfortunately, this program stopped in 1982. The ongoing SIMBIOS results will dovetail nicely with the previous work (which also had Coastal Zone Color Scanner (CZCS) coverage) for looking at any long-term changes in the Gulf of Maine hydrography, bio-optics, and biogeochemistry.

Balch, William M.↗
NASA NTRSDate not supplied

Mapping Aerosol Lidar Ratios Over Ocean Using Constrained Retrievals and A Global Aerosol Model

The current NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) algorithms assign one lidar ratio (i.e., extinction-to-backscatter ratio; LR) value globally for each of the seven tropospheric aerosol types. In a future data products release, the CALIPSO project aims to improve these algorithms through the development of regional and seasonal LR climatologies. In this work, aerosol LRs are inferred through Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) backscatter profiles constrained by collocated aerosol optical depth (AOD) from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data. This analysis is subsampled for those profiles that are cloud-free and contain only one CALIOP-classified aerosol type (e.g., marine). The CALIOP profiles are then collocated with aerosol volume fractions obtained through Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulations. In this presentation, we show that the twelve-year (June 2006-August 2018) mean spatial distributions of inferred aerosol LRs for CALIOP-classified marine aerosols correspond inversely with patterns of GOCART sea salt volume fraction (SSVF). For example, smaller SSVFs (< 65%) and larger LRs (> 55 sr), are found near land masses. This is indicative of the influence of over-land aerosols (e.g., pollution and biomass burning smoke). In the remote oceans (i.e., regions likely less impacted by non-sea salt aerosols), larger SSVFs (> 95%) and smaller LRs (< 25 sr) are found. The developed relationship between the GOCART SSVFs and MODIS AOD constrained LRs (polynomial fit intersect values of ~56 sr for SSVF of 0% and ~21 sr for SSVF of 100%) is used to produce model-assisted climatological LR maps on seasonal scales. Additionally, we show maps of inferred LRs from constrained retrievals using the CALIPSO Ocean Derived Column Optical Depth (ODCOD) product and comparisons with those from the MODIS analyses. The technique demonstrated in this study not only benefits CALIPSO in the near-term, but similar methods can be applied to future spaceborne elastic backscatter lidars with collocated passive sensors, such as those associated with the upcoming NASA Atmosphere Observing System (AOS).

Travis Toth↗
NASA NTRS1994

An improved cloud retrieval algorithim using HIRS2-MSU radiance measurements

Cloud-top heights and cloud amounts are produced as part of the operational processing of polar-satellite data at the National Environmental Satellite Data and Information Service (NESDIS). These products were compared with similar products from the air force's real-time nephanalysis (RTNEPH), from the International Satellite Cloud Climatology Project, and from NASA Goddard's processing of satellite data. It was found that the amount of high-level cloud was too small in the NESDIS results, while the amount of low-level cloud was too large. An examination of the NESDIS algorithm revealed that the differences in cloud distributions were caused by the selection of channels used for the cloud retrievals. Cloud retrievals are most accurate at the levels at which the channels that are used are most sensitive. In addition, it was found that no one pair of channels was best at all levels. A new procedure was developed that varied the channels as a function of an initial estimate of the cloud height. This procedure produced improved cloud retrievals that were then compared with the RTNEPH results. The comparison showed that the two methods provide similar retrievals of cloud height and amount.

Mcmillin, Larry↗
NASA NTRS2017

Role of Forcing Uncertainty and Background Model Error Characterization in Snow Data Assimilation

Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 (Advanced Microwave Scanning Radiometer 2) instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.

assimilation↗
NASA NTRS2020

Combining MODIS LAI with ICESat-Based Canopy Heights Improves Spaceborne Estimates of Vegetation Roughness Length for Momentum

Most land-surface models require parameterization of vertical wind profiles within the atmospheric boundary layer. For vegetated surfaces, it is common to assume a logarithmic profile in the surface layer, which includes estimates of vegetation roughness length for momentum (z0) and zero-plane displacement height (d0). This study finds that remotely-sensed forest canopy heights improve estimates of aerodynamic roughness length for momentum using a previously-developed representation of the roughness sublayer (Raupach 1992; Jasinski et al. 2005). Resulting roughness products consist of two datasets: 1) 14 years of 8-day snapshots of the global land surface at a nominal spatial resolution of 500-meters for users who wish to retain full temporal resolution and interannual variability; and 2) multiyear averages of the 8-day snapshots, here referred to as "climatologies" of roughness, which retain underlying seasonality. Both products are suitable for use in data assimilation and reanalyses such as the National Climate Assessment Land Data Assimilation System (NCA-LDAS), for which these products were initially developed.

Borak, Jordan S.↗
NASA NTRS2014

The Development of a Long-Term, Continually Updated Global Solar Resource at 10 km Resolution: Preliminary Results From Test Processing and Continuing Plans

Background: Considering the likelihood of global climatic weather pattern changes and the global competition for energy resources, there is an increasing need to provide improved and continuously updated global Earth surface solar resource information. Toward this end, a project was funded under the NASA Applied Science program involving the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC), National Renewable Energy Laboratory (NREL), the State University of New York/Albany (SUNY) and the NOAA National Climatic Data Center (NCDC) to provide NREL with a global long-term advanced global solar mapping production system for improved depiction of historical solar resources and variability and to provide a mechanism for continual updates of solar resource information. This new production system is made possible by the efforts of NOAA and NASA to completely reprocess the International Satellite Cloud Climatology Project (ISCCP) data set that provides satellite visible and infrared radiances together with retrieved cloud and surface properties on a 3-hourly basis beginning from July 1983. The old version of the ISCCP data provided this information for all the world TMs available geosynchronous satellite systems and NOAA TMs AVHRR data sets at a 30 km effective resolution. This new version aims to provide a new and improved satellite calibration at an effective 10 km resolution. Thus, working with SUNY, NASA will develop and test an improved production system that will enable NREL to continually update the Earth TM solar resource. Objective and Methods: In this presentation, we provide a general overview of this project together with samples of the new solar irradiance mapped data products and comparisons to surface measurements at various locations across the world. An assessment of the solar resource values relative to calibration uncertainty and assumptions are presented. Errors resulting assumptions in snow cover and background aerosol amount are described. These uncertainties and the statistics of the agreement between the measurements and new satellite estimates are also reviewed and compared to other solar data sets. Findings and Conclusions: Preliminary results show that insolation values show an overall small bias (less than 1%) with a RMS of 25% relative to surface measurements. Exceptions at certain locations were found and will be discussed relative to the uncertainties identified above. Lastly, we will identify the next steps in the development and improvement of this production system including some accuracy goals in preparation for ultimate delivery to NREL.

Stackhouse, P.↗
NASA NTRS2017

Importance of a Priori Vertical Ozone Profiles for TEMPO Air Quality Retrievals

Ozone (O3) is a toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address the limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME (Global Ozone Monitoring Experiment), GOME-2, and OMI (Ozone Monitoring Instrument). This algorithm is suggested to use a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB-Clim) O3 climatology). This study evaluates the TB-Clim dataset and model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time data assimilation model products (NASA GMAO's (Global Modeling and Assimilation Office) operational GEOS-5 (Goddard Earth Observing System, Version 5) FP (Forecast Products) model and reanalysis data from MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2)) and a full chemical transport model (CTM), GEOS-Chem. In this study, vertical profile products are evaluated with surface (0-2 kilometers) and tropospheric (0-10 kilometers) TOLNet (Tropospheric Ozone Lidar Network) observations and the theoretical impact of individual a priori profile sources on the accuracy of TEMPO O3 retrievals in the troposphere and at the surface are presented. Results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles, model-simulated profiles from a full CTM resulted in more accurate tropospheric and surface-level O3 retrievals from TEMPO when compared to hourly and daily-averaged TOLNet observations. Furthermore, it is shown that when large surface O3 mixing ratios are observed, TEMPO retrieval values at the surface are most accurate when applying CTM a priori profile information compared to all other data products.

Priori↗
NASA NTRSDate not supplied

The Next-Generation Version 3 GPCP Products

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

precipitation↗
NASA NTRS1999

TRMM-Based Merged Precipitation Analyses

This paper describes results of using Tropical Rainfall Measuring Mission (TRMM) information as the key calibration tool in a merged analysis on a 1X1 latitude/longitude monthly scale based on multiple satellite sources and raingauge analyses. The TRMM-based product is compared with surface-based validation data sets and the community-based 20-year Global Precipitation Climatology Project (GPCP)monthly analyses. The TRMM-based merged analysis uses the TRMM information to calibrate the estimates from SSM/I and geosynchronous IR observations and merges those estimates together with the TRMM and gauge information to produce accurate rainfall estimates with the increased sampling provided by the combined satellite information. This TRMM merged analysis uses the combined instrument (Precipitation Radar [PR] and TRMM Microwave Imager [TMI]) retrieval of Haddad as the TRMM estimate with which to calibrate the other satellite estimates. This TRMM Combined instrument (TCI) estimate is shown to produce very similar absolute values to the other main TRMM products. The TRMM and other satellites merged analysis compares favorably to the atoll data set of Morrissey for the months of 1998 with a very small positive bias of 2%. However, comparison with the preliminary results from the TRMM ground validation radar information at Kwajalein atoll in the western Pacific Ocean shows a 26% positive bias. Therefore, absolute magnitudes from TRMM and/or the ground validation need to be treated with care at this point. A month by month comparison of the TRMM merged analysis and the GPCP analysis indicates very similar patterns, but with subtle differences in magnitude. Focusing on the Pacific Ocean ITCZ one can see the TRMM-based estimates having higher peak values and lower values in the ITCZ periphery. These attributes also show up in the statistics, where GPCP>TRMM at low values (below 10 mm/d) and TRMM>GPCP at high values (greater than 15 mm/d). Integrated over the 37N-37S belt for all of 1998 the TRMM value is 3.1 mm/day compared to 2.9 mm/day for GPCP, with the results when confined to ocean being 3.3 and 3.0, respectively. Therefore, TRMM has approximately 10% higher tropical oceanic precipitation than GPCP. Examples of TRMM monthly fields in analysis of geographic and temporal variations will also be discussed. Results for 1998 indicate that TRMM has a different ratio of western Pacific to eastern Pacific rainfall than GPCP, perhaps indicating that TRMM's ability to discern the vertical structure of the rain may lead to better estimates in the eastern Pacific. In addition the rainfall anomalies associated with the '98-'99 ENSO event are shown to be larger with the TRMM results as compared to the GPCP analyses.

Adler, Robert↗
NASA NTRS2011

MISR Interactive Explorer (MINX) : Production Digitizing to Retrieve Smoke Plume Heights and Validating Heights Against Lidar Data

The height at which smoke from a wildfire is injected into the atmosphere is an important parameter for climatology, because it determines how far the smoke can be transported. Using the MINX program to analyze MISR (Multi-angle Imaging Spectro-Radiometer) data, I digitized wildfire smoke plumes to add to an existing database of these heights for use by scientists studying smoke transport and plume dynamics. In addition to using MINX to do production digitizing of heights, I assisted in gathering lidar data for an ongoing validation of MINX and helped evaluate those data.

smoke plume↗
NASA NTRS1995

Validation of Global Climatologies of Trace Gases Using NASA Global Tropospheric Experiment (GTE) Data

Methane is an important trace gas because it is a greenhouse gas that affects the oxidative capacity of the atmosphere. It is produced from biological and anthropogenic sources, and is increasing globally at a rate of approximately 0.6% per year [Climate Change 1992, IPCC]. By using National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory (NOAA/CMDL) ground station data, a global climatology of methane values was produced. Unfortunately, because the NOAA/CMDL ground stations are so sparse, the global climatology is low resolution. In order to compensate for this low resolution data, it was compared to in-situ flight data obtained from the NASA Global Tropospheric Experiment (GTE). The smoothed ground station data correlated well with the flight data. Thus, for the first time it is shown that the smoothing process used to make global contours of methane using the ground stations is a plausible way to approximate global atmospheric concentrations of the gas. These verified climatologies can be used for testing large-scale models of chemical production, destruction, and transport. This project develops the groundwork for further research in building global climatologies from sparse ground station data and studying the transport and distribution of trace gases.

Courchaine, Brian↗
NASA NTRS2012

Toward a Combined SAGE II-HALOE Aerosol Climatology: An Evaluation of HALOE Version 19 Stratospheric Aerosol Extinction Coefficient Observations

Herein, the Halogen Occultation Experiment (HALOE) aerosol extinction coefficient data is evaluated in the low aerosol loading period after 1996 as the first necessary step in a process that will eventually allow the production of a combined HALOE/SAGE II (Stratospheric Aerosol and Gas Experiment) aerosol climatology of derived aerosol products including surface area density. Based on these analyses, it is demonstrated that HALOE's 3.46 microns is of good quality above 19 km and suitable for scientific applications above that altitude. However, it is increasingly suspect at lower altitudes and should not be used below 17 km under any circumstances after 1996. The 3.40 microns is biased by about 10% throughout the lower stratosphere due to the failure to clear NO2 but otherwise appears to be a high quality product down to 15 km. The 2.45 and 5.26 micron aerosol extinction coefficient measurements are clearly biased and should not be used for scientific applications after the most intense parts of the Pinatubo period. Many of the issues in the aerosol data appear to be related to either the failure to clear some interfering gas species or doing so poorly. For instance, it is clear that the 3.40micronaerosol extinction coefficient measurements can be improved through the inclusion of an NO2 correction and could, in fact, end up as the highest quality overall HALOE aerosol extinction coefficient measurement. It also appears that the 2.45 and 5.26 micron channels may be improved by updating the Upper Atmosphere Pilot Database which is used as a resource for the removal of gas species otherwise not available from direct HALOE measurements. Finally, a simple model to demonstrate the promise of mixed visible/infrared aerosol extinction coefficient ensembles for the retrieval of bulk aerosol properties demonstrates that a combined HALOE/SAGE II aerosol climatology is feasible and may represent a substantial improvement over independently derived data sets.

Thomason, L. W.↗
NASA NTRS2013

Progress Towards Deriving an Improved Long-Term Global Solar Resource

This paper describes an ongoing project to provide the National Renewable Energy Laboratory (NREL) with a global long-term advanced global solar mapping production system for improved depiction of historical solar resources and to provide a mechanism for continual updates. This new production system is made possible by the efforts of NASA and NOAA to completely reprocess the International Satellite Cloud Climatology Project (ISCCP) data set that provides satellite visible and infrared radiances together with retrieved cloud and surface properties on a 10 km, 3-hourly basis beginning July 1983. We provide a general overview of this project, samples of the new solar irradiance mapped data products, and comparisons to surface measurements. Samples of the use of the SUNY-Albany solar irradiance algorithm applied to the ISCCP data show very good agreement with high quality surface measurements. We identify the next steps in the production of the data set.

Cox, Stephen J.↗
DOE OSTI2022

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations

Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Evaluating a Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product.TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.

TEMPO↗
NASA NTRS1995

Global climatology and variability of potential new production estimated from remote sensing of sea-surface temperature

During this project we have collected numerous shipboard data-bases of oceanic nitrate and silicate versus temperature for both equatorial and coastal upwelling regions. These cruises all have accompanying N-15 measurements of new production. The inverse relationships between nutrients and temperatures have been determined and are being used to obtain surface nutrient fields from sea surface temperatures measured remotely by satellite borne sensors- i.e. AVHRR data from NOAA satellites contained in the MCSST data set for the world ocean provided by the University of Miami. The images and data derived from space in this way show the strong seasonal fluctuations and interannual el Nino fluctuations of the nitrate field. the nitrate data has been used to make estimates of new production for the equatorial pacific which are compared with shipboard measurements when available. The importance of silicate as a nutrient driving new production and the ratio of nitrate to silicate has been discovered to be crucial to better understand the causes of new production variability, so we have added these parameters to our study and have begun to make estimates of these for the equatorial Pacific, derived from the weekly averaged sea surface temperatures (SSTs).

Dugdale, Richard C.↗
NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗