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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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NASA NTRS2005

Studying the Diurnal Cycle of Convection Using a TRMM-Calibrated Infrared Rain Algorithm

The development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale is presented. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics. The technique makes use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of nonraining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the last being important for the calculation of vertical profiles of latent heating. The diurnal cycle of rainfall, as well as the division between convective and Stratiform rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. Results from five years of PR data will show the global-tropical partitioning of convective and stratiform rainfall.

Negri, Andrew J.↗
NASA NTRS2002

A Method to Retrieve Rainfall Rate over Land from TRMM Observations

Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations over mesoscale convective systems (MCSs) reveal that there are localized maxima in the rain rate with a scale of about 10 to 20 km that represent thunderstorms (Cbs). Some of these Cbs are developing or intense, while others are decaying or weak. These Cbs constitute only about 20 % of the rain area of a given MCS. Outside of Cbs, the average rain rate is much weaker than that within Cbs. From an analysis of the PR data, we find that the spatial distribution of rain and its character, convective or stratiform, is highly inhomogeneous. This complex nature of rain exists on a scale comparable to that of a Cb. The 85 GHz brightness temperature, T85, observations of the TRMM Microwave Imager (TMI) radiometer taken over an MCS reflect closely the PR rain rate pattern over land. Local maxima in rain rate shown by PR are observed as local minima in T85. Where there are no minima in T85, PR observations indicate there is light rain. However, the TMI brightness temperature measurements (Tbs) have poor ability to discriminate convective rain from stratiform rain. For this reason, a TMI rain retrieval procedure that depends primarily on the magnitude of Tbs performs poorly. In order to retrieve rain rate from TMI data on land one has to include the spatial distribution information deduced from the T85 data in the retrieval method. Then, quantitative estimation of rain rate can be accomplished. A TMI rain retrieval method developed along these lines can yield estimates of rain rate and its frequency distribution which agree closely with that given by PR. We find the current TRMM project TMI (Version 5) rain retrieval algorithm on land could be improved with the retrieval scheme developed here. To support the conceptual frame work of the rain retrieval method developed here, a theoretical analysis of the TMI brightness temperatures in convective and stratiform regions is presented.

Prabhakara, C.↗
NASA NTRS2003

Satellite Detection of Precipitation over the North Pacific

Intense extratropical winter cyclones often impact the West Coast of North America with strong winds and heavy precipitation. Several times during a winter season, short-term forecasts (24 - 48 hours) of these storms are seriously deficient with central pressure errors in the 10's of hPa and surface low position errors in the 100's of km. For example, 48-hr sea level pressure errors (forecast - observation) at buoy 46005 off the Oregon coast for the 2001 - 2002 winter season is plotted. In addition, two times the standard deviation (determined from pressure errors from the last four winter seasons) are also shown. It is evident from this figure that large forecast errors (i.e. greater than 10 hPa) occurred about 10 times this past winter at buoy 46005 with three events where the errors were 20 hPa. Beside large forecast errors of sea level pressure, numerical forecasts of precipitation for land falling cyclones can also be flawed. This is due in large part to the lack of accurate precipitation information over the ocean. Therefore, remote sensing techniques are the only viable option for obtaining accurate information on the distribution and intensity of precipitation over the North Pacific. Due to the radiative characteristics of precipitation sized hydrometeors at microwave frequencies, microwave sensors are able to detect precipitation over oceanic regions. Past studies have demonstrated the utility of passive microwave rainrate data for locating intense rainfall in rapidly deepening cyclones, in detecting developing polar mesocyclones and in determining frontal bands. There are currently many sources of microwave rainrate data: the Special Sensor Microwave Imager (SSM/I) (currently flying on three platforms), the Advanced Microwave Sounding Unit (AMSU-B) (currently flying on NOAA-15, NOAA-16, and NOAA-17), and the Tropical Rainfall Measuring Mission Microwave Imager (TMI). Data will soon be available from the Advanced Microwave Radiometer-EOS (AMSR-E) on the Aqua platform. In this paper, we present a new technique for mapping rainrate distributions over the North Pacific utilizing rainrate estimates from several microwave sensors and upper-tropospheric winds derived from geosynchronous satellite IR data. The goal of this work is to develop a way to obtain high temporal and spatial rainfall information over the North Pacific. This information will be used to support the verification of model derived precipitation distributions and to support the analysis of in situ measurements of rainfall during the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE) field campaigns.

Smith, Jeremy↗
NASA NTRS2002

A Method to Retrieve Rainfall Rate Over Land from TRMM Microwave Imager Observations

Over tropical land regions, rain rate maxima in mesoscale convective systems revealed by the Precipitation Radar (PR) flown on the Tropical Rainfall Measuring Mission (TRMM) satellite are found to correspond to thunderstorms, i.e., Cbs. These Cbs are reflected as minima in the 85 GHz brightness temperature, T85, observed by the TRMM Microwave Imager (TMI) radiometer. Because the magnitude of TMI observations do not discriminate satisfactorily convective and stratiform rain, we developed here a different TMI discrimination method. In this method, two types of Cbs, strong and weak, are inferred from the Laplacian of T85 at minima. Then, to retrieve rain rate, where T85 is less than 270 K, a weak (background) rain rate is deduced using T85 observations. Furthermore, over a circular area of 10 km radius centered at the location of each T85 minimum, an additional Cb component of rain rate is added to the background rain rate. This Cb component of rain rate is estimated with the help of (T19-T37) and T85 observations. Initially, our algorithm is calibrated with the PR rain rate measurements from 20 MCS rain events. After calibration, this method is applied to TMI data taken from several tropical land regions. With the help of the PR observations, we show that the spatial distribution and intensity of rain rate over land estimated from our algorithm are better than those given by the current TMI-Version-5 Algorithm. For this reason, our algorithm may be used to improve the current state of rain retrievals on land.

Prabhakara, C.↗
NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗
NASA NTRS2000

Sampling Errors in Monthly Rainfall Totals for TRMM and SSM/I, Based on Statistics of Retrieved Rain Rates and Simple Models

Estimates from TRMM satellite data of monthly total rainfall over an area are subject to substantial sampling errors due to the limited number of visits to the area by the satellite during the month. Quantitative comparisons of TRMM averages with data collected by other satellites and by ground-based systems require some estimate of the size of this sampling error. A method of estimating this sampling error based on the actual statistics of the TRMM observations and on some modeling work has been developed. "Sampling error" in TRMM monthly averages is defined here relative to the monthly total a hypothetical satellite permanently stationed above the area would have reported. "Sampling error" therefore includes contributions from the random and systematic errors introduced by the satellite remote sensing system. As part of our long-term goal of providing error estimates for each grid point accessible to the TRMM instruments, sampling error estimates for TRMM based on rain retrievals from TRMM microwave (TMI) data are compared for different times of the year and different oceanic areas (to minimize changes in the statistics due to algorithmic differences over land and ocean). Changes in sampling error estimates due to changes in rain statistics due 1) to evolution of the official algorithms used to process the data, and 2) differences from other remote sensing systems such as the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I), are analyzed.

Bell, Thomas L.↗
DOE OSTI2025

Experimental Study of a Novel Semi-Transparent Bi-Facial Agri-Voltaic System: Initial Results

Solar (photovoltaic)-agriculture practices have experienced notable progress across several countries over the past few decades, creating a field widely known as agrivoltaics. This co-location practice has proved effective for certain crops that prefer partial shade like tomatoes, lettuce, and peppers, although it is less effective for crops needing more sun. Availability of natural sunlight with a specific spectral signature is necessary for agricultural practices as many plant species require an optimum-level and quality of sunlight for photosynthesis. The goal of this work is to model and validate ground-level irradiance for a semi-transparent bifacial solar collector equipped with a custom spectral reflector. By introducing intentional gaps between opaque solar cells, we hope to allow 50% of visible light to pass through the module. Additionally, this technology could reduce the soil temperature by 2° C, improve moisture retention, and enhance yield by providing partial solar radiation. In our model, we can calculate the total solar irradiance for the plants as a function of the cell distribution, material absorption, height of the panel and time of year with a spatial resolution of 0.0625 m2. When comparing theoretical estimates to experimental measurements of photosynthetically active radiation (PAR), we find a discrepancy of 32-48%, and a discrepancy of 19-38% after correction with TMY data.

McGraw, Duncan (ORCID:0000000256388118)↗
NASA NTRS2019

Understand and Process ATC Restrictions in the National Airspace System: Part 2

Continues Part 1 discussion by using ATD-2 technology as a stand-in for expected TMI entries in the future TFDM system. This topic will focus on the importance of stream insertion and how flights can meet both tactical and strategic constraints and will briefly cover the TMI data being made available in the new TTP SWIM feed.

Staudenmeier, Bob↗
NASA NTRSDate not supplied

Detecting Hail from Space: Using a Multi-Frequency Passive-Microwave Retrieval to Analyze the Global Climatology and Diurnal Cycle of Severe Hail

Severe hail poses myriad threats to society, causing extensive damage to infrastructure and agriculture. As hail is severe, relatively infrequent, and highly localized, it is difficult to measure in-situ and if left unresolved in models and precipitation retrievals, hail can cause large errors and uncertainties. The difficulty in measuring hail in situ and the inconsistency of surface-based hail reporting drive the motivation to use spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We leverage the scattering signatures of severe hail in spaceborne passive-microwave datasets paired with surface hail reports to construct a multi-frequency hail retrieval using Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We use this retrieval to construct global passive-microwave climatologies of severe hail using the TRMM, GPM, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and extend into the pre-TRMM era to the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. We also leverage the sensors in inclined orbits to assess the diurnal variability of severe hail globally and the effect the diurnal cycle has on the detection of hail by sensors in sun-synchronous orbit. The goal is to construct a robust, multi-decade multi-satellite climatology of hail. As part of the NASA Disasters Applied Sciences program, we assess these climatologies against other satellite severe weather datasets and use these climatologies in collaboration with stakeholders and end-users to help them assess risk, and improve the prediction, preparation, and response to severe storms around the world.

Sarah D Bang↗
NASA NTRS1999

Improving Assimilated Global Data Sets using TMI Rainfall and Columnar Moisture Observations

A global analysis 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 products contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. In this study, we show that assimilating precipitation and total precipitable water (TPW) retrievals derived from the TRMM Microwave Imager (TMI) improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the large-scale circulation produced by the Goddard Earth Observing System (GEOS) data assimilation system (DAS). In particular, assimilating TMI rain improves clouds and radiation in areas of active convection, as well as the latent heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. The improved analysis also improves short-range forecasts in the tropics. Ensemble forecasts initialized with the GEOS analysis incorporating TMI rain rates and TPW yield smaller biases in tropical precipitation forecasts beyond 1 day and better 500 hPa geopotential height forecasts up to 5 days. Results of this study demonstrate the potential of using high-quality space-borne rainfall and moisture observations to improve the quality of assimilated global data for climate analysis and weather forecasting applications

Hou, Arthur Y.↗
NASA NTRS2010

Changes in the TRMM Version 7 Space/Time Averaged Level 3 Data Products Based on GPROF TMI Swath-Based Precipitation Retrievals

TRMM has three level 3 (space/time averaged) data products that aggregate level 2 TRMM Microwave Imager (TMI) GPROF precipitation retrievals. These three products are TRMM 3A12, which is a monthly accumulation of 2A12 the GPROF swath retrieval product; TRMM 3B31, which is a monthly accumulation of 2A12 and 2B31 the combined retrieval product that uses both Precipitation Radar (PR) and TMI data; and 3G68 and its variants, which provide hourly retrievals for TMI, PR and combined. The 3G68 products are packaged as daily files but provide hourly information at 0.5 deg x 0.5 deg resolution globally, 0.25 deg x 0.25 deg globally, or 0.1 deg x 0.1 deg over Africa, Australia and South America. This paper will present early information of the changes in the v7 TMI GPROF level 2 retrievals that have an impact on the level 3 accumulations. This paper provides an analysis of the effect the 2A12 GPROF changes have on 3G68 products. In addition, it provides a comparison between the TRMM level 3 products that use the TMI GPROF swath retrievals.

Stocker, Erich↗
NASA NTRS1993

System services and architecture of the TMI satellite mobile data system

The North American Mobile Satellite Service (MSS) system being developed by AMSC/TMI and scheduled to go into service in early 1995 will include the provision for real time packet switched services (mobile data service - MDS) and circuit switched services (mobile telephony service - MTS). These services will utilize geostationary satellites which provide access to mobile terminals (MT's) through L-band beams. The MDS system utilizes a star topology with a centralized data hub (DH) and will support a large number of mobile terminals. The DH, which accesses the satellite via a single Ku band beam, is responsible for satellite resource management, for providing mobile users with access to public and private data networks, and for comprehensive network management of the system. This paper describes the various MDS services available for the users, the ground segment elements involved in the provisioning of these services, and a summary description of the channel types, protocol architecture, and network management capabilities provided within the system.

Gokhale, D.↗
NASA NTRS2002

The Impact of TRMM Data on Numerical Forecast of Mesoscale Systems

The impact of surface rainfall data derived from the TRMM Microwave Image (TMI) on the numerical forecast of mesoscale systems is evaluated. A series of numerical experiments are performed that assimilate TMI rainfall data into the Penn State University/National Centers for Atmospheric Research (PSU/NCAR) Mesoscale Model version 5 (MM5) using a four-dimensional variational data assimilation (4DVAR) technique. Experiments are conducted incorporating TMI rainfall data into the mesoscale model to improve hurricane initialization. It is found that assimilation of rainfall data into the model is beneficial in producing a more realistic eye and rain bands and also helps to improve the intensity forecast for the hurricane. Further 4DVAR experiments are performed on mesoscale convective systems (MCSs). Detailed results and related issues will be presented during the conference.

Pu, Zhao-Xia↗
NASA NTRS1990

A land mobile satellite data system

The Telesat Mobile Incorporated (TMI) Mobile Data System (MDS) was developed to apply satellite technology to the transportation industry's requirement for a fleet management system. It will provide two-way messaging and automatic position reporting capabilities between dispatch centers and customers' fleets of trucks. The design was based on the Inmarsat L-Band space segment with system link parameters and margins adjusted to meet the land mobile satellite channel characteristics. The system interfaces with the Teleglobe Des Laurentides earth station at Weir, Quebec. The signaling protocols were derived from the Inmarsat Standard C packet signalling system with unique trucking requirements incorporated where necessary.

Kent, John D. B.↗
NASA NTRS1999

Comparisons of Monthly Oceanic Rainfall Derived from TMI and SSM/I

A technique for estimating monthly oceanic rainfall rate using multi-channel microwave measurements has been developed. There are three prominent features of this algorithm. First, the knowledge of the form of the rainfall intensity probability density function used to augment the measurements. Second, utilizing a linear combination of the 19.35 and 22.235 GHz channels to de-emphasize the effect of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35 and 22.235 GHz brightness temperature histograms. This technique is applied to the SSM/I data since 1987 to infer monthly rainfall for the Global Precipitation Climatology Project (GPCP). A modified version of this algorithm is now being applied to the TRMM Microwave Imager (TMI) data. TMI data with better spatial resolution and 24 hour sampling (vs. sun-synchronized sampling, which is limited to two narrow intervals of local solar time for DMSP satellites) prompt us to study the similarity and difference between these two rainfall estimates. Six months of rainfall data (January to June 1998) are used in this study. Means and standard deviations are calculated. Paired student t-tests are administrated to evaluate the differences between rainfall estimates from SSM/I and TMI data. Their differences are discussed in the context of global satellite rainfall estimation.

Chang, A. T. C.↗
NASA NTRS1999

A TRMM Microwave Radiometer Rain Rate Estimation Method with Convective and Stratiform Discrimination

Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometer brightness temperature data in the 85 GHz channel (T85) reveal distinct local minima (T85min) in a regional map containing a Mesoscale Convective System (MCS). This is because of relatively small footprint size (approximately 5.5 km) and strong extinction properties in this channel of the TMI. A map of surface rain rate for that region, deduced from simultaneous measurements made by the Precipitation Radar (PR) on board the TRMM satellite, reveals that these T85(sub min), produced by scattering, correspond to local PR rain maxima. Utilizing the PR rain rate map as a guide, we infer empirically from TMI data the presence of three different kinds of thunderstorms or Cbs. These Cbs are classified as young, mature, and decaying types, and are assumed to have a scale of about 20 km on the average. Two parameters are used to classify these three kinds of Cbs based on the T85 data: a) the magnitude of scattering depression deduced from local T85(sub min) and b) the mean horizontal gradient of T85 around such minima. Knowing the category of a given Cb, we can estimate the rain rate associated with it. Such estimation is done with the help of relationships linking T85min to rain rate in each Cb type. Similarly, a weak background rain rate in all the areas where T85 is less than 260 K is deduced with another relationship linking T85 to rain rate. In our rain retrieval model, this background rain constitutes stratiform rain where the Cbs are absent. Initially, these relationships are optimized or tuned utilizing the PR and TMI data of a few MCS events. After such tuning, the model is applied to independent MCS cases. The areal distribution of light (1-10 mm/hr), moderate (10-20 mm/hr), and intense (>= 20 mm/hr) rain rates are retrieved satisfactorily. Accuracy in the estimates of the light, moderate, and intense rain areas and the mean rain rates associated with such areas in these independent MCS cases is on the average about 15 %. Taking advantage of this ability of our retrieval method, one could derive the latent heat input into the atmosphere over the 760 km wide swath of the TMI radiometer in the tropics.

Prabhakara, C.↗
NASA NTRS1999

TMI Rain Rate Estimation Over Land and Ocean Utilizing Convective and Stratiform Discrimination

Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometer brightness temperature data in the 85 GHz channel (T85) reveal distinct local minima in a regional map containing a Mesoscale Convective System (MCS). This is because of relatively small footprint size (approximately 5.5 km) and strong extinction properties in this channel of the TMI. A map of rain rate for that region, deduced from simultaneous measurements made by the Precipitation Radar (PR) on board the TRMM satellite, reveals that these T85 minima, produced by scattering, correspond to local PR rain maxima. Utilizing the PR rain rate map as a guide, we infer from TMI data the presence of three different kinds of thunderstorms or Cbs. They are young, mature, and decaying Cbs that have a scale of about 20 km on the average. Two parameters enable us to infer these three kinds of Cbs objectively: a) the magnitude of scattering depression deduced from local T85 minima and b) the mean horizontal gradient of T85 around such minima. Knowing the category of a given Cb, we can estimate the rain rate associated with it. Such estimation is done with the help of relationships linking T85 minimum to rain rate in each Cb type. Similarly, a weak background rain rate in all the areas where T85 is less than 260 K is deduced with another relationship linking T85 to rain rate. In our rain retrieval model, this background rain constitutes the stratiform rain where the Cbs are absent. Initially, these relationships are optimized or tuned utilizing the PR and TMI data of a few MCS events. After such tuning, the model is applied to independent MCS cases. The areal distribution of light (1-10 mm/hr), moderate (10-20 mm/hr), and intense (> 20 mm/hr) rain rates are retrieved satisfactorally. Accuracy in the estimates of the light, moderate and intense rain areas and the mean rain rates associated with such areas in these independent MCS cases is on the average about 15%. Taking advantage of this ability of our retrieval method, one could derive the latent heat input into the atmosphere over the 760 km wide swath of the TMI radiometer in the tropics.

Prabhakara, C.↗