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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.

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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980-47 2015 using the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah Version 3.3 land surface model at 0.125 degreesx0.125 degrees forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth and irrigation products. Mean annual trends are reported using the nonparametric Mann-Kendall test at p<0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends, and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the Upper Great Plains and Northeast to -1 to -9 in the West and South; net radiation flux trends range from +0.05 to +0.20 W/m(exp2)/yr in the East to -0.05 to -0.20 in the West; U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from West to East. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%; Trends in latent heat flux were hardly affected, RMSE decreasing only 0.17 to 0.16 W/m(exp 2)/yr, while trend detection increased 2%; NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gaged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

Snow depth↗
DOE OSTI2017

Asymmetric winter warming advanced plant phenology to a greater extent than symmetric warming in an alpine meadow

1. The warming of terrestrial high-latitude ecosystems, while increasing, will likely be asymmetric across seasons—where winter non-growing seasons will warm more than summer-growing seasons. Asymmetric winter warming in temperature-sensitive ecosystems may delay spring phenological events by reducing the opportunity that a plants’ chilling requirement is met. Similarly, symmetric warming can advance spring phenology. 2. To explore the impact of asymmetric warming on plant phenology, we applied a year-round warming and a winter warming treatment to our experimental plots. Over a 2-year period, we monitored leaf-out and flowering phenology for 11 plant species. 3. There was variation among species, however, both winter and year-round warming, advanced the leaf-out day and the first flowering day relative to the control treatment. So, winter warming advanced leaf-out and flowering phenology by 11.1 (±2.4) and 12.6 (±2.9) days respectively. However, year-round warming had less of an impact advancing leaf-out and flowering phenology by 5.1 (±2.1) and 10.0 (±3.0) days respectively. 4. Our study provides direct evidence that asymmetric winter warming has a larger impact on plant phenology than symmetric year-round warming. Increasing soil temperature in the winter from below to above freezing temperatures advanced the spring phenology of alpine plants. Winter warming increased soil temperature more than year-round warming, which explains why phenology advanced under winter warming more than under year-round warming. In addition, early or mid-season flowering plant species displayed different phenology strategies in warmer winters. 5. Relative to other ecosystems, alpine ecosystems such as the Tibetan Plateau will likely respond to asymmetric warming given the higher amplitude of winter temperature increases due to climatic warming. Conclusively, our data indicate that seasonal variation in warming should be considered when predicting and modelling the response of alpine ecosystems to climatic change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Rotor Speed Dependent Yaw Control of Wind Turbines Based on Empirical Data

When extracting energy from the wind using horizontal-axis upwind wind turbines, a primary condition for maximum power yield is the ability to align the rotor axis with the dominating wind direction. Attempts have been made to improve yaw alignment by applying advanced measurement techniques such as LIDARs. This study is focused at assessing the current performance of an operating turbine and exploring how the yaw alignment can be improved using existing measurements. By analyzing available turbine and met mast data a correction scheme for the original yaw alignment system is synthesized. The correction scheme is applied and it is seen that with the correction scheme in place, the power yield below rated is raised 1-5 percent. Furthermore, results indicate that blade load variations are decreased when the correction scheme is applied. The results are associated with uncertainties due to the amount of available data and the wind site climate. Further work should be focused at gathering more experimental data.

17 WIND ENERGY↗
NASA NTRS1983

Aerosol, radiation, and climate

Airborne, spaceborne, and ground-based measurements are used to study the radiative and climatic effects of aerosols. The data, which are modelled with a hierarchy of radiation and climate models, and their implications are summarized. Consideration is given to volcanic aerosols, polar stratospheric clouds, and the Arctic haze. It is shown that several types of aerosols (volcanic particles and the Arctic haze) cause significant alterations to the radiation budget of the regions where they are located.

Pollack, J. B.↗
NASA NTRS2024

The Economics of Farming Expansion in the Brazilian Cerrado Under Possible Effects of Climate Change

This analysis assesses the financial viability of legally investing in native Cerrado vegetation deforestation for crop production, considering climate change. The study uses data from twelve different crop models based on three different climate models to predict potential future crop yields in cleared land for growing soy and maize. The outcomes show that in many micro-regions, investments in clearing land for crop production would destroy economic value, that is, generate a negative net present value because of low/negative and volatile cashflows driven primarily by future yields as affected by climate. Our analysis was carried out based on present agricultural practices and technology. As climate changes, farmers may adapt their practices, which can lead to more resilient and productive crops, or grow different crops, which could provide better returns on investment in clearing land than the ones resulting from our analysis. Despite various uncertainties, farmers, policy makers and financial institutions should be aware of the climatic and financial risks associated with land clearing in Brazil, mainly in micro-regions in which all scenarios resulted in negative outcomes in the investment analysis.

agricultural expansion↗
NASA NTRS1994

Special relativity effects for space-based coherent lidar experiments

There is a great need to develop a system that can measure accurately atmospheric wind profiles because an accurate data of wind profiles in the atmosphere constitutes single most input for reliable simulations of global climate numerical methods. Also such data helps us understand atmospheric circulation and climate dynamics better. Because of this need for accurate wind measurements, a space-based Laser Atmospheric Winds Sounder (LAWS) is being designed at MSFC to measure wind profiles in the lower atmosphere of the earth with an accuracy of 1 m/s at lower altitudes to 5m/s at higher altitudes. This system uses an orbiting spacecraft with a pulsed laser source and measures the Doppler shift between the transmitted and received frequencies to estimate the atmospheric wind velocities. If a significant return from the ground (sea) is possible, the spacecraft speed and height are estimated from it and these results and the Doppler shift are then used to estimate the wind velocities in the atmosphere. It is expected that at the proposed wavelengths, there will be enough backscatter from the aerosols but there may no be significant return from the ground. So a coherent (heterodyne) detection system is being proposed for signal processing because it can provide high signal to noise ratio and sensitivity and thus make the best use of low ground return. However, for a heterodyne detection scheme to provide the best results, it is important that the receiving aperture be aligned properly for the proposed wind sounder, this amounts to only a few microradians tolerance in alignment. It is suspected that the satellite motion relative to the ground may introduce errors in the order of a few microradians because of special relativity. Hence, the problem of laser scattering off a moving fixed target when the source and receiver are moving, which was not treated in the past in the literature, was analyzed in the following, using relativistic electrodynamics and applied to the case of the space-based coherent lidar, assuming flat ground. Here an interest in developing analytical expression for the location of the receiving point for the return with respect to the satellite, receiving angle and Doppler shift in frequency and amount of tip, all as measured in the satellite moving coordinate system and the diffuse scattering angle at the ground which does not require any compensation. All the three cases of retro-reflection, specular reflection and diffuse scattering by the ground should be treated though retro-reflection and diffuse scattering are more important.

Raogudimetla, V. S.↗
DOE OSTI2015

A Global Repository for Planet-Sized Experiments and Observations

Greenland’s ice loss doubled between 1996 and 2005. Nine of the ten warmest years on record have occurred since 2000. Carbon dioxide levels in the air are at their highest in 650,000 years. Are these phenomena related, and if so, what can they tell us about the Earth’s climate in the coming years and decades? The answer will require sophisticated computer models—and a way of dealing with the data they produce. Past climate patterns can be discerned by examining variables such as temperature and precipitation records, lake sediments, tree rings, and ice cores. Projecting future regional and global climate patterns, however, requires computer models that can simulate the complex interplay between physical, chemical, and biological processes occurring in the atmosphere, ocean, and on land and sea ice. These models are extremely computationally demanding. Over the past several decades, an ongoing revolution in high-capacity, high-performance computing has accelerated climate modeling. In 1998, a 1-year simulation of global climate using a 300- kilometer grid could be run in a day. In 2002, it took 4 or 5 hours. In 2015, this same model can be run on a high-end desktop in minutes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A hierarchical evaluation framework for assessing climate simulations relevant to the energy-water-land nexus (Final Report)

The overarching goal was to construct a hierarchy of new and well-tested metrics and analysis tools that support both fundamental and use-inspired research. Motivating this goal was a convergence of needs from climate scientists and stakeholders alike for a systematic, robust framework of model evaluation and diagnosis to provide scientific insights, inform model development, support best practices for the use of climate model outputs, and facilitate communication of climate information in the evolving landscapes of multi-model, multi-resolution, and large ensemble simulations that generate terabytes of data for any single climate run. Steps toward attaining these goals benefited from expertise and established capability of the project team, which included leadership of the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Downscaling Experiment (CORDEX), development of hierarchical model evaluation approaches, and successful research in the analysis and diagnosis of climate model skill, as well as the understanding and modeling of regional climate processes in North America. As part of the overarching goal, the project worked to disseminate a suite of methodologies, algorithms, and software components that the wider community can employ to advance climate science and applications. With rigorous demonstration, the evaluation framework and the mix of standard and high risk / high reward approaches helped form the basis for future development of a computationally enabled user-friendly system for community use.

17 WIND ENERGY↗
DOE OSTI2021

Climate-Relevant Gas Absorption Properties from AWARE and Other ARM Spectral Measurements (Final Report)

The objective of this project has been to use measurements from the Atmospheric Emitted Radiance Interferometer (AERI) deployed at ARM sites to improve our knowledge of uncertain infrared spectroscopic parameters of importance to climate, remote sensing, and data assimilation. A primary focus has been the water vapor continuum in the infrared atmospheric window. Radiation codes used to predict climate and weather base their representation of the water vapor continuum in this window on the MT_CKD model, but in this spectral region the MT_CKD water vapor continuum absorption coefficients were derived almost two decades ago by an analysis of AERI measurements for conditions with a limited range of precipitable water vapor (PWV) and temperature values, leading to uncertainty in the derived coefficients. This project has included a new, comprehensive analysis of all aspects of the water vapor continuum in the atmospheric window -- the self continuum, the foreign continuum, and the self continuum temperature dependence, all resolved spectrally. Having a wide range of PWV amounts and temperatures from deployments of the ARM Mobile Facility for GoAmazon as well as the more than a decade of AERI measurements at SGP has provided an appropriate foundation for such a comprehensive analysis.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Equatorial Annual Oscillation with QBO-driven 5-year Modulation in NCEP Data

An analysis is presented of the zonal wind and temperature variations supplied by the National Center for Environmental Prediction (NCEP), which have been assimilated in the Reanalysis and the Climate Prediction Center (CCP) data sets. The derived zonal-mean variations are employed. Stimulated by modeling studies, the data are separated into the hemispherically symmetric and anti-symmetric components, and spectral analysis is applied to study the annual 12-month oscillation and Quasi-biennial Oscillation (QBO). For data samples that cover as much as 40 years, the results reveal a pronounced 5-year modulation of the symmetric AO in the lower stratosphere, which is confined to equatorial latitudes. This modulation is also inferred for the temperature variations but extends to high latitudes, qualitatively consistent with published model results. A comparison between different data samples indicates that the signature of the 5-year oscillation is larger when the QBO of 30 months is more pronounced. Thus there is circumstantial evidence that this periodicity of the QBO is involved in generating the oscillation. The spectral analysis shows that there is a weak anti-symmetric 5-year oscillation in the zonal winds, which could interact with the large antisymmetric A0 to produce the modulation of the symmetric AO as was shown in earlier modeling studies. According to these studies, the 30-month QBO tends to be synchronized by the equatorial Semi-annual Oscillation (SAO), and this would explain why the inferred 5-year modulation is observed to persist and is phase locked over several cycles.

Mayr, H. G.↗
DOE OSTI2026

Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes

Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099

Li, Bonan↗
NASA NTRSDate not supplied

Fusion of Hyperspectral Sounder Products Via Spectral Fingerprinting Methodology

Satellite based measurements of top-of-atmosphere (TOA) spectral radiances in the infrared (IR) region have been in existence for almost two decades and are expected to be continued in the following decades. The data from multiple hyper-spectral IR sounders can therefore be combined to build a long-term data record to further global scale climate trend research. Challenges associated with the fusion of data from different sensors come from the stability and consistency requirement on the climate record. The direct radiance observations from different sounders need to be homogenized by reconciling the differences in calibration, spectral response function (SRF), and spatial-temporal sampling. When geophysical variables derived from radiances measured by different sounders are combined to form long-term climate records, the impacts of any inconsistencies between overlapping measurements on the retrieval must be carefully assessed in order to estimate the uncertainty of the corresponding climate anomalies/trends derived. This paper presents a novel climate fingerprinting methodology and establishes a rigorously-defined inverse relationship that allows us to efficiently evaluate the change in essential climate variables from the change in spectral radiances measured in prescribed spatial and temporal averaging scales. The inverse spectral fingerprinting relationship is constructed based on a unified spectral kernel scheme, providing a direct means for quantifying the potential discontinuity in the derived climate anomalies due to inconsistencies between overlapping measurements. We show in this paper a sample application of using the spectral fingerprinting scheme to derive long-term, global-scale surface temperatures from the Climate Hyperspectral Infrared Radiance Product (CHIRP) and quantify the inter-satellite biases.

Wan Wu↗
DOE OSTI2018

Near-real-time environmental monitoring and large-volume data collection over slow communication links

Climate change studies are one of the most important aspects of modern science and related experiments are getting bigger and more complex. One such experiment is the Spruce and Peatland Responses Under Changing Environments conducted in northern Minnesota. The SPRUCE experimental mission is to assess ecosystem-level biological responses of vulnerable, high-carbon terrestrial ecosystems to a range of climate warming manipulations and an elevated CO 2 atmosphere. This manipulation experiment generates a lot of observational data and requires a reliable on-site data collection system, dependable methods to transfer data to a robust scientific facility, and real-time monitoring capabilities. Furthermore this publication shares our experience of establishing a near-real-time data collection and monitoring system via a satellite link using the not very well-known possibilities of PakBus protocol.

47 OTHER INSTRUMENTATION↗
NASA NTRS2014

Secular Climate Change on Mars: An Update Using One Mars Year of MSL Pressure Data

The South Polar Residual Cap (SPRC) on Mars is an icy reservoir of CO2. If all the CO2 trapped in the SPRC were released to the atmosphere the mean annual global surface pressure would rise by approximately 20 Pa. Repeated MOC and HiRISE imaging of scarp retreat within the SPRC led to suggestions that the SPRC is losing mass. Estimates for the loss rate vary between 0. 5 Pa per Mars Decade to 13 Pa per Mars Decade. Assuming 80% of this loss goes directly into the atmosphere, an estimate based on some modeling (Haberle and Kahre, 2010), and that the loss is monotonic, the global annual mean surface pressure should have increased between approximately 1-20 Pa since the Viking mission (approximately 20 Mars years ago). Surface pressure measurements by the Phoenix Lander only 2.5 Mars years ago were found to be consistent with these loss rates. Last year at this meeting we compared surface pressure data from the MSL mission through sol 360 with that from Viking Lander 2 (VL-2) for the same period to determine if the trend continues. The results were ambiguous. This year we have a full Mars year of MSL data to work with. Using the Ames GCM to compensate for dynamics and environmental differences, our analysis suggests that the mean annual pressure has decreased by approximately 8 Pa since Viking. This result implies that the SPRC has gained (not lost) mass since Viking. However, the estimated uncertainties in our analysis are easily at the 10 Pa level and possibly higher. Chief among these are the hydrostatic adjustment of surface pressure from grid point elevations to actual elevations and the simulated regional environmental conditions at the lander sites. For these reasons, the most reasonable conclusion is that there is no significant difference in the size of the atmosphere between now and Viking. This implies, but does not demand, that the mass of the SPRC has not changed since Viking. Of course, year-to-year variations are possible as implied by the Phoenix data. Given that there has been no unusual behavior in the climate system as observed by a variety of spacecraft at Mars since Phoenix, its seems more likely that the Phoenix data simply did not have a long enough record to accurately determine annual mean pressure changes as Haberle and Kahre (2010) cautioned. In the absence of a strong signal in the MSL data, we conclude that if the SPRC is loosing mass it is not going into the atmosphere reservoir.

Phoenix data↗
NASA NTRS2014

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

Climate Forcing Data↗
DOE OSTI2014

Reconstruction of the South Atlantic Subtropical Dipole index for the past 12,000 years from surface temperature proxy

Climate indices based on sea surface temperature (SST) can synthesize information related to physical processes that describe change and variability in continental precipitation from floods to droughts. The South Atlantic Subtropical Dipole index (SASD) is based on the distribution of SST in the South Atlantic and fits these criteria. It represents the dominant mode of variability of SST in the South Atlantic, which is modulated by changes in the position and intensity of the South Atlantic Subtropical High. Here we reconstructed an index of the South Atlantic Ocean SST (SASD-like) for the past twelve thousand years (the Holocene period) based on proxy-data. This has great scientific implications and important socio-economic ramifications because of its ability to infer variability of precipitation and moisture over South America where past climate data is limited. For the first time a reconstructed index based on proxy data on opposite sides of the SASD-like mode is able to capture, in the South Atlantic, the significant cold events in the Northern Hemisphere at 12.9–11.6 kyr BP and 8.6–8.0 ky BP. These events are related, using a transient model simulation, to precipitation changes over South America.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Climate Change: A New Metric to Measure Changes in the Frequency of Extreme Temperatures using Record Data

Consensus on global warming is the result of multiple and varying lines of evidence, and one key ramification is the increase in frequency of extreme climate events including record high temperatures. Here we develop a metric- called "record equivalent draws" (RED)-based on record high (low) temperature observations, and show that changes in RED approximate changes in the likelihood of extreme high (low) temperatures. Since we also show that this metric is independent of the specifics of the underlying temperature distributions, RED estimates can be aggregated across different climates to provide a genuinely global assessment of climate change. Using data on monthly average temperatures across the global landmass we find that the frequency of extreme high temperatures increased 10-fold between the first three decades of the last century (1900-1929) and the most recent decade (1999-2008). A more disaggregated analysis shows that the increase in frequency of extreme high temperatures is greater in the tropics than in higher latitudes, a pattern that is not indicated by changes in mean temperature. Our RED estimates also suggest concurrent increases in the frequency of both extreme high and extreme low temperatures during 2002-2008, a period when we observe a plateauing of global mean temperature. Using daily extreme temperature observations, we find that the frequency of extreme high temperatures is greater in the daily minimum temperature time-series compared to the daily maximum temperature time-series. There is no such observable difference in the frequency of extreme low temperatures between the daily minimum and daily maximum.

Munasinghe, L.↗
NASA NTRS2000

Improving Global Analysis and Short-Range Forecast Using Rainfall and Moisture Observations Derived from TRMM and SSM/I Passive Microwave Instruments

The Global Precipitation Mission, a satellite project under consideration as a follow-on to the Tropical Rainfall Measuring Mission (TRMM) by the National Aeronautics and Space Agency (NASA) in the United States, the National Space Development Agency (NASDA) in Japan, and other international partners, comprises an improved TRMM-like satellite and a constellation of 8 satellites carrying passive microwave radiometers to provide global rainfall measurements at 3-hour intervals. The success of this concept relies on the merits of rainfall estimates derived from passive microwave radiometers. This article offers a proof-of-concept demonstration of the benefits of using, rainfall and total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imager (TMI) and 2 Special Sensor Microwave/Imager (SSM/I) instruments. Global analyses that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. We show that assimilating the 6-h averaged TMI and SSM/I surface rainrate and TPW retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper tropospheric moisture in the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System, as verified against radiation measurements by the Clouds and the Earth's Radiant Energy System (CERES) instrument and brightness temperature observations by the TIROS Operational Vertical Sounder (TOVS) instruments. Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the tropics, while TPW assimilation leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Ensemble forecasts initialized with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and precipitation in the tropics. This study offers a compelling illustration of the potential of using rainfall and TPW information derived from passive microwave instruments to significantly improve the quality of 4-dimensional global datasets for climate analysis and weather forecasting applications.

Hou, Arthur Y.↗