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NASA NTRS2015

Simulated Impacts of Climate Change on Water Use and Yield of Irrigated Sugarcane in South Africa

Reliable predictions of climate change impacts on water use, irrigation requirements and yields of irrigated sugarcane in South Africa (a water-scarce country) are necessary to plan adaptation strategies. Although previous work has been done in this regard, methodologies and results vary considerably. The objectives were (1) to estimate likely impacts of climate change on sugarcane yields, water use and irrigation demand at three irrigated sugarcane production sites in South Africa (Malelane, Pongola and La Mercy) for current (1980-2010) and future (2070-2100) climate scenarios, using an approach based on the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols; and (2) to assess the suitability of this methodology for investigating climate change impacts on sugarcane production. Future climate datasets were generated using the Delta downscaling method and three Global Circulation Models (GCMs) assuming atmospheric CO2 concentration [CO2] of 734 ppm(A2 emissions scenario). Yield and water use were simulated using the DSSAT-Canegro v4.5 model. Irrigated cane yields are expected to increase at all three sites (between 11 and 14%), primarily due to increased interception of radiation as a result of accelerated canopy development. Evapotranspiration and irrigation requirements increased by 11% due to increased canopy cover and evaporative demand. Sucrose yields are expected to decline because of increased consumption of photo-assimilate for structural growth and maintenance respiration. Crop responses in canopy development and yield formation differed markedly between the crop cycles investigated. Possible agronomic implications of these results include reduced weed control costs due to shortened periods of partial canopy, a need for improved efficiency of irrigation to counter increased demands, and adjustments to ripening and harvest practices to counter decreased cane quality and optimize productivity. Although the Delta climate data downscaling method is considered robust, accurate and easily-understood, it does not change the future number of rain-days per month. The impacts of this and other climate data simplifications ought to be explored in future work. Shortcomings of the DSSAT-Canegro model include the simulated responses of phenological development, photosynthesis and respiration processes to high temperatures, and the disconnect between simulated biomass accumulation and expansive growth. Proposed methodology refinements should improve the reliability of predicted climate change impacts on sugarcane yield.

Climate change↗
NASA NTRS2015

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition↗
NASA NTRS2016

Weather and Climate Change Impacts on Human Mortality in Bangladesh

Weather and climate profoundly affect human health. Several studies have demonstrated a U-, V-, or J-shaped temperature-mortality relationship with increasing death rates at the lower and particularly upper end of the temperature distribution. The objectives of this study were (1) to analyze the relationship between temperature and mortality in Bangladesh for different subpopulations and (2) to project future heat-related mortality under climate change scenarios. We used (non-)parametric Generalized Additive Models adjusted for trend, season and day of the month to analyze the effect of temperature on daily mortality. We found a decrease in mortality with increasing temperature over a wide range of values; between the 90th and 95th percentile an abrupt increase in mortality was observed which was particularly pronounced for the elderly above the age of 65 years, for males, as well as in urban areas and in areas with a high socio-economic status. Daily historical and future temperature values were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset. This dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5). The derived dose-response functions were used to estimate the number of heat-related deaths occurring during the 1990s (1980-2005), the 2020s (2011-2040) and the 2050s (2041-2070). We estimated that excess deaths due to heat will triple from the 1990s to the 2050s, with an annual number of 0.5 million excess deaths in 1990 to and expected number of 1.5 millions in 2050.

mortality↗
NASA NTRS2019

Comparing Flood-Producing Catastrophic Hurricanes in GEOS: A Study of Juan 1985 and Harvey 2017

Hurricanes Juan (1985) and Harvey (2017) bear remarkable similarities because of the exceptional amount of accumulated precipitation over coastal regions in the Gulf, consequent to the peculiar and rare situation in which a hurricane stalls for several days on a coastal area, displaying minimal progress. This is particularly dangerous because: a) terrestrial drag affect only part of the circulation so that weakening is not as fast as when there is rapid motion inland; b) a part of the circulation remains over water and as such low-level moisture flux convergence is maintained; c) precipitation lasts for a very long time over the same area; d) moisture recycling from flooded areas further contributes to maintaining flood conditions. From the forecast perspective, hurricanes with track singularities such as stalling points, loops, or multiple landfalls over the same region are exceptionally difficult to predict because they result from small variations of opposing mechanisms and are therefore intrinsically unstable. Intensity fluctuations and track singularities are strongly coupled. The representation of fluxes undergoing differential drag (ocean vs. land) inside the same circulation is critically dependent upon resolution. Recent modeling development efforts within the NASA Global Modeling and Assimilation Office (GMAO) have brought the horizontal resolution of the global cubed-sphere Goddard Earth Observing System (GEOS) atmospheric model down to 12 km. This has led to innovative findings in the field of tropical cyclone (TC) modeling within a global framework. The GEOS atmospheric model and data assimilation system (DAS), used to produce the MERRA-2 (Modern Era Retrospective analysis for Research and Applications) reanalysis, has a "Replay" capability that adds a forcing term to the model equations to constrain it to follow a specified trajectory. This capability is used to produce global, downscaled versions of the 50 km MERRA-2 reanalysis at a 12.5 km resolution. The full DAS and replay modes both use the Incremental Analysis Update or IAU for applying an analysis increment to the model's background state four times each day at 00Z, 06Z, 12Z and 18Z. In a full DAS mode, a six-hour free run of the model is made (Predictor Step), writing states that will be used as backgrounds or first-guesses for the analysis centered on 00Z. The Analysis system then produces its best estimate of the 00Z state by blending backgrounds with observations. The difference between this best estimate and the model's original first guess at 00Z is treated as an analysis increment and added uniformly as a forcing term to the model during a second, now forced, run of the 12Z to 03Z window, which produces the assimilated results. Replay mode is very similar, except only the 00Z background is needed from the predictor segment, which is thus half as long, and instead of doing an analysis, a previously analyzed state at 00Z is read in. The increment is then computed and applied to the corrector step in the same way as in the original DAS. When downscaling horizontal spatial filters can be applied to the "analysis" increment used to force the Corrector step, so as to reproduce the full meteorological state or selectively choose or discard certain forcings. In this presentation, full global hindcast simulations of Juan and operational forecasts of Harvey are compared.

Flooding↗
NASA NTRS2021

Assessing Disaggregated SMAP Soil Moisture Products in the United States

A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the Soil Moisture Active Passive (SMAP) Enhanced product(SPL2SMPE) from 9 km to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from Moderate Resolution Imaging Spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid -this MODIS-derived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatiotemporal coverage and accuracy using in situ measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network (USDA-SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network(NOAA-CRN). Results were also compared with the baselineSPL2SMPE and the SMAP/Sentinel-1 (SPL2SMAPS) 1kmproduct. Overall, the unbiased root mean square error (ubRMSE)of the disaggregated SM at the CVS using the TI approach is approximately 0.04 m3=m3, which is the SMAP mission requirement for the baseline products. The TI approach out performs the SMAP/Sentinel SL2SMAPS 1km product by approximately0.02 m3=m3. Over the agriculture/crop areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMPE andSPL2SMAPS byabout0.01 and 0.02 m3=m3, indicating its advantage in these areas. However, a drawback of this approach is that there are data gaps due to cloud cover as optical sensors cannot have a clear view of the land surface.

Soil moisture↗
NASA NTRS2020

Supporting Information for ”The 2019-2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes

The model configuration employs the modified International Geosphere Biosphere Programme (IGBP) MODIS 20 category landcover data, soil parameters derived from the International Soil Reference and Information Centre, and the Shuttle Radar Topography Mission based elevation, slope, and aspect data. Statistical downscaling approaches are used to transform the coarse resolution MERRA2 meteorological inputs to 1km. The input meteorological fields of air temperature, humidity, surface pressure, wind, downward shortwave radiation, and downward longwave radiation are downscaled to 1km by adjusting for terrain differences in elevation, slope, and aspect. The high resolution monthly precipitation climatology from WorldClim is used to spatially disaggregate input MERRA2 precipitation to 1km. The initial conditions for the model simulations are generated from a long spinup of NoahMP starting from year 2000. All model integrations and evaluations are conducted using the NASA Land Information System and the Land surface Verification Toolkit.

Sujay V Kumar↗
NASA NTRSDate not supplied

Using Time Series Data Products to Support ASHRAE’s Historic and Future Climate Data Needs through NASA’s POWER Web Services

To address the data needs for resilience and sustainability of building systems within ASHRAE, the NASA Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data on a long-term, global scale. Solar data from several NASA projects and meteorological data from NASA assimilation models have been reformatted and disseminated to the public via a user friendly web GIS-enabled based data portal (https://power.larc.nasa.gov) in selectable data formats, immediately amenable to key industry wide decision support tools such as EnergyPlus. Time series of potential climate conditions from downscaled climate simulations are being made available for similar statistics and formats. An emphasis is placed upon obtaining and utilizing time series data to conduct analysis of building system performance for current and planning building systems for energy savings and greenhouse gas emissions. This presentation has the learning following learning objectives for the ASHRAE community: 1) Learn how NASA POWER’s web service suite can provide supplementary solar and surface meteorological data parameters, including a discussion of uncertainty. 2) Learn how downscaled climate scenario information can be provided to evaluate potential future requirements. The presentation is part of a forum entitled, "Weather Data for Large-Scale Building Energy Modeling".

time series↗
NASA NTRS2023

Use of Machine Learning and Principal Component Analysis to Retrieve Nitrogen Dioxide (NO 2 ) With Hyperspectral Imagers and Reduce Noise in Spectral Fitting

Nitrogen dioxide (NO 2 ) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO 2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO 2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO 2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO 2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO 2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO 2 retrievals. Nevertheless, the NO 2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO 2 retrievals. Once trained, NNs can also substantially speed up NO 2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

NO2↗
NASA NTRSDate not supplied

NASA POWER: Providing Present and Future Climate Services Based on NASA Data for the Energy, Agricultural, and Sustainable Buildings Communities

NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the renewable energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data at various temporal levels from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). Data users can access these data either through an intuitive data viewer, image services fully integrable with GIS analysis, connections in the cloud through an Amazon Web Services S3 Bucket, or fully customizable access through an API. Data provided by POWER has been successfully used by decision makers to support actions that address climate change. For example, POWER data has been used to remotely monitor solar array fields and integrated in a sizing tool for off-grid solar and storage systems. POWER data has also been coupled with key building decision tools to support design and retrofitting of building energy systems for energy efficiency and reduction of greenhouse gases. POWER is now developing climate services to provide time series of the projected future evolution of key quantities that interest our users, such as heating/cooling degree days, temperature, wind speed, and downwelling solar flux. We demonstrate the potential of the new climate services by presenting here a range of possible future changes in these quantities at different NASA centers across the continental United States. These data services are based on downscaled climate model data from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data set. We highlight the important insights that new climate services can provide. Our climate services will help our user communities quantify the impacts of climate change to support their key decisions in planning for the future, both inside and outside the Federal Government, especially for decisions in renewable energy and in building heating and cooling.

Bradley Hegyi↗
NASA NTRS2024

Landslide Hazard is Projected to Increase Across High Mountain Asia

High Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that landslide hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with a nonlinear response relative to increasing global temperatures. However, these changes are geographically varied. This article maps probable changes to landslide hazard, as shown by a landslide hazard indicator (LHI) derived from downscaled precipitation and temperature. In order to capture the nonlinear response of slopes to extreme precipitation, a simple machine-learning model was trained on a database of landslides across High Mountain Asia to develop a regional LHI. This model was applied to statistically downscaled data from the 30 members of the Seamless System for Prediction and Earth System Research large ensembles to produce a range of possible outcomes under the Shared Socioeconomic Pathways 2-4.5 and 5-8.5. The LHI reveals that landslide hazard will increase in most parts of High Mountain Asia. Absolute increases will be highest in already hazardous areas such as the Central Himalaya, but relative change is greatest on the Tibetan Plateau. Even in regions where landslide hazard declines by year 2100, it will increase prior to the mid-century mark. However, the seasonal cycle of landslide occurrence will not change greatly across High Mountain Asia. Although substantial uncertainty remains in these projections, the overall direction of change seems reliable. These findings highlight the importance of continued analysis to inform disaster risk reduction strategies for stakeholders across High Mountain Asia.

Thomas A Stanley↗
NASA NTRS2013

Projected Climate Impacts to South African Maize and Wheat Production in 2055: A Comparison of Empirical and Mechanistic Modeling Approaches

Crop model-specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic (MMs) and empirical models (EMs) are rare despite both being used widely in this field. We combined MMs and EMs to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in South Africa under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EMs projected larger yield losses or smaller gains than MMs. The EMs' median-projected maize and wheat yield changes were 3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM. The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region (EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM) substantially overestimated the extent of current suitability. The substantial water-use efficiency gains simulated by the MMs under elevated CO2 accounted for much of the EMMM difference, but EMs may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EMs may show larger climate change losses than MMs. Crop forecasting efforts should expand to include EMMM comparisons to provide a fuller picture of crop-climate response uncertainties.

DSSAT↗
NASA NTRS2014

Sensitivity of Air Quality to Potential Future Climate Change and Emissions in the United States and Major Cities

Simulated present and future air quality is compared for the years 2006e2010 and 2048e2052 over the contiguous United States (CONUS) using the Community Multi-scale Air Quality (CMAQ) model. Regionally downscaled present and future climate results are developed using GISS and the Weather Research Forecasting (WRF) model. Present and future emissions are estimated using MARKAL 9R model. O3 and PM(sub 2.5) sensitivities to precursor emissions for the years 2010 and 2050 are calculated using CMAQDDM (Direct Decoupled Method). We find major improvements in future U.S. air quality including generally decreased MDA8 (maximum daily 8-hr average O3) mixing ratios and PM(sub 2.5) concentrations and reduced frequency of NAAQS O3 standard exceedances in most major U.S. cities. The Eastern and Pacific U.S. experience the largest reductions in summertime seasonal average MDA8 (up to 12 ppb) with localized decreases in the 4th highest MDA8 of the year, decreasing by up to 25 ppb. Results from a Climate Penalty (CP) scenario isolate the impact of climate change on air quality and show that future climate change tends to increase O3 mixing ratios in some regions of the U.S., with climate change causing increases of over 10 ppb in the annual 4th highest MDA8 in Los Angeles. Seasonal average PM(sub 2.5) decreases (2-4 microgram m(exp -3)) over the Eastern U.S. are accounted for by decreases in sulfate and nitrate concentrations resulting from reduced mobile and point source emissions of NO(sub x) and SO(sub x).

Projecting emissions↗
NASA NTRS2022

Future Climate Change Impact on Wildfire Danger Over the Mediterranean: the Case of Greece

Recent studies have shown that temperature and precipitation in the Mediterranean are expected to change, contributing to longer and more intense summer droughts that even extend out of season. In connection to this, the frequency of forest fire occurrence and intensity will likely increase. In the present study, the changes in future fire danger conditions are assessed for the different regions of Greece using the Canadian fire weather index (FWI). Gridded future climate output as estimated from three regional climate models from the Coordinated Regional Downscaling Experiment are utilized. We use three representative concentration pathways (RCPs) consisting of an optimistic emissions scenario where emissions peak and decline beyond 2020 (RCP2.6), a middle-of-the-road scenario (RCP4.5) and a pessimistic scenario, in terms of mitigation where emissions continue to rise throughout the century (RCP8.5). Based on established critical fire FWI threshold values for Greece, the future change in days with critical fire danger were calculated for different areas of Greece domains. The results show that fire danger is expected to progressively increase in the future especially in the high-end climate change scenario with southern and eastern regions of Greece expected to have up to 40 additional days of high fire danger relative to the late 20th century, on average. Crete, the Aegean Islands, the Attica region, as well as parts of Peloponnese are predicted to experience a stronger increase in fire danger.

Forest fires↗
NASA NTRS2022

What Does Global Land Climate Look Like at 2°C Warming?

Constraining an increase in global mean temperature below 2°C compared to pre-industrial levels is critical to limiting dangerous and cascading impacts of anthropogenic climate change. Understanding future climatic changes and their spatial heterogeneity at 2°C warming is thus important for policy makers to prepare actionable adaptation and mitigation plans by identifying where and to what extent lives and livelihoods will be impacted. This study uses the recently released NASA Earth eXchange Global Daily Downscaled Projections (NEX-GDDP) CMIP6 data to provide a broad overview of projected changes in six key climate variables and two climate impact indicators at a time when warming exceeds 2°C. Analysis of global mean temperature changes indicates the 2040s as the decade when most CMIP6 models reach 2°C warming with respect to a pre-industrial period (1850–1900). During the 2040s, we find that global mean temperature, precipitation, relative humidity, downwelling shortwave and longwave radiation, and wind speed over land under the high emission scenario are projected to change by +2.8°C, +22.4 mm/year, −0.73%, −2.23 , +15.9 W/m 2 , and −0.04 m/s, respectively. Many of the future changes are expected to exacerbate climate impacts including heat stress and fire danger. Our analysis shows geographic patterns of policy-relevant climatic changes, as parts of the globe will experience significant climate impacts even if the goal to keep warming below 2°C goal is achieved. Our results highlight the urgent need for further studies focused on identifying key hotspots and advancing region-specific actionable adaptation and mitigation plans.

GDDP↗
NASA NTRS2023

Regional Analysis of the 2015–16 Lower Mekong River Basin Drought Using NASA Satellite Observations

Study region Lower Mekong River Basin (LMRB) Study focus Satellite remote sensing products are widely used for monitoring droughts. Using NASA satellite sensors of precipitation (Global Measurement Mission, GPM), soil moisture (Soil Moisture Active and Passive, SMAP), and terrestrial water storage (Gravity Recovery and Climate Experiment, GRACE), this study evaluates the historical drought in the LMRB during 2015–16. SMAP soil moisture was validated against in-situ soil moisture, and GPM precipitation and SMAP soil moisture were cross-validated with streamflow observations. The spatiotemporal dynamics of soil moisture were also examined in different ranges of catchment areas. In performing the analysis, we used lagged correlations between hydrological variables and the indices of the Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI). New hydrological insights for the regions Spatio-temporal patterns of drought in 2015–16 were examined from the entire basin to small watersheds. A mismatch occurs when using GRACE data to study droughts in small watersheds (many of the small watersheds would be a fraction of the few 100 km2 spatial resolutions of GRACE pixel). In smaller watersheds, hydrological drought (SSI) was closely defined with SMAP soil moisture downscaled to 1 km rather than the meteorological drought index (SPI). By leveraging satellite-based observations across a range of spatial scales, this study highlights the utility of Earth observations in informing water resources and land management decisions at the regional scale.

Mekong↗
NASA NTRS1984

Missions/Planning Panel

A panel discussion was held to develop a viewpoint of space power technology needs and state of readiness for future mission scenarios. Among the points made in the discussion, it was agreed that missions, particularly the far term ones, do serve to drive technology; however, as the missions become nearer term, issues of schedule and cost severely limit the willingness to accept risk. There are, in fact, no rewards to a mission manager for introducing new technology. Mission downscaling is the usual response to technology limitations. All panelists agreed that there exists a serious gap between when technologists feel their job is done and what mission managers need for decision. Typically a two to three year engineering development gap exists. It is essential to take technologies to the engineering model level and conduct a flight demonstration to close this gap. All agreed that increased effort should be made to achieve stronger interactions between planners and technologists and that workshops like the present one are a step in the right direction. Technologists need mission credibility and vice versa.

Fordyce, J. S.↗
NASA NTRS1985

Simulation of mesoscale convective response

Results are reported from a transfer of thermodynamic and dynamic data downscale from a two-dimensional mesoscale model to a two-dimensional cloud model and a subsequent examination of the differential convective response of the cloud model to a mesoscale structure. The mesoscale model included a high resolution PBL formulation, with convective forces expressed in a profile of exchange coefficients over the height of the PBL and the magnitude of the surface heat flux. Account was also taken of forcing by long- and short-wave radiation, surface forcing by the surface energy budget, the terrain shape, and possible wave reflection at the top boundary. The slab-symmetric cloud model possessed subgrid-scale features, five types of moisture terms, and a subroutine for accretion processes. The mesoscale environment was observed to have a significant impact on convective response, i.e., the vertical velocity and the cloud water. Various factors which were not included in the study and which must be considered are discussed.

Mcnider, R. T.↗
NASA NTRS1990

Biconic cargo return vehicle with an advanced recovery system. Volume 1: Conceptual design

The conceptual design of the biconic Cargo Return Vehicle (CRV) is presented. The CRV will be able to meet all of the Space Station Freedom (SSF's) resupply needs. Worth note is the absence of a backup recovery chute in case of Advanced Recovery System (ARS) failure. The high reliability of ram-air parachutes does not warrant the penalty weight that such a system would create on successful missions. The CRV will launch vertically integrated with an Liquid Rocket Booster (LRB) vehicle and meets all NASA restrictions on fuel type for all phases of the mission. Because of the downscaled Orbital Maneuvering Vehicle (OMV) program, the CRV has been designed to be able to transfer cargo by docking directly to the Space Station Freedom as well as with OMV assistance. The CRV will cover enough crossrange to reach its primary landing site, Edwards Airforce Base, and all secondary landing sites with the exception of one orbit. Transportation back to KSC will be via the Boeing Super Guppy. Due to difficulties with man-rating the CRV, it will not be used in a CERV role. A brief summary of the CRV's specifications is given.

Source record↗