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NASA NTRS1986

Experimental forecasts of El Nino

A deterministic numerical model of the coupled evolution of the tropical ocean and atmosphere was used to forecast all El Nino/Southern Oscillation (ENSO) events from 1970 to 1986. More particularly, the model, originally developed for studying large-scale ocean-atmosphere interactions in the tropics, successfully predicted the characteristics of the spatial and temporal structure of ENSO observed in the study interval. The model indicated that rainfall moving eastward over the Pacific slackens the surface winds that would otherwise cool the eastern Pacific by drawing up cooler subsurface waters. The oceanic thermocline increases, a poleward flow of westerly flowing warn waters deplets the equatorial warm water reservoir, and sea surface temperatures decline. These ENSO conditions are statistically tractable with the model several months in advance, provided upper ocean layer thermal data are available.

Cane, M. A.↗
NASA NTRS1999

The Status of the Tropical Rainfall Measuring Mission (TRMM) after 2 Years in Orbit

The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on November 27, 1997, and data from all the instruments first became available approximately 30 days after launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, in related modeling applications and in new datasets tailored specifically for these applications. This paper reports the latest results regarding the calibration of the TRMM Microwave Imager, (TMI), Precipitation Radar (PR) and Visible and Infrared Sensor (VIRS). For the TMI, a new product is in place that corrects for a still unknown source of radiation leaking in to the TMI receiver. The PR calibration has been adjusted upward slightly (by 0.6 dBZ) to better match ground reference targets, while the VIRS calibration remains largely unchanged. In addition to the instrument calibration, great strides have been made with the rainfall algorithms as well, with the new rainfall products agreeing with each other to within less than 20% over monthly zonally averaged statistics. The TRMM Science Data and Information System (TSDIS) has responded equally well by making a number of new products, including real-time and fine resolution gridded rainfall fields available to the modeling community. The TRMM Ground Validation (GV) program is also responding with improved radar calibration techniques and rainfall algorithms to provide more accurate GV products which will be further enhanced with the new multiparameter 10 cm radar being developed for TRMM validation and precipitation studies. Progress in these various areas has, in turn, led to exciting new developments in the modeling area where Data Assimilation, and Weather Forecast models are showing dramatic improvements after the assimilation of observed rainfall fields.

Kummerow, C.↗
NASA NTRS1992

Detection of the Earth with the SETI microwave observing system assumed to be operating out in the galaxy

This paper estimates the maximum range at which radar signals from the Earth could be detected by a search system similar to the NASA Search for Extraterrestrial Intelligence Microwave Observing Project (SETI MOP) assumed to be operating out in the galaxy. Figures are calculated for the Targeted Search, and for the Sky Survey parts of the MOP, both operating, as currently planned, in the second half of the decade of the 1990s. Only the most powerful terrestrial transmitters are considered, namely, the planetary radar at Arecibo in Puerto Rico, and the ballistic missile early warning systems (BMEWS). In each case the probabilities of detection over the life of the MOP are also calculated. The calculation assumes that we are only in the eavesdropping mode. Transmissions intended to be detected by SETI systems are likely to be much stronger and would of course be found with higher probability to a greater range. Also, it is assumed that the transmitting civilization is at the same level of technological evolution as ours on Earth. This is very improbable. If we were to detect another technological civilization, it would, on statistical grounds, be much older than we are and might well have much more powerful transmitters. Both factors would make detection by the NASA MOP a much more likely outcome.

NASA Center ARC↗
NASA NTRS1977

Remote sensing-aided systems for snow qualification, evapotranspiration estimation, and their application in hydrologic models

The design of general remote sensing-aided methodologies was studied to provide the estimates of several important inputs to water yield forecast models. These input parameters are snow area extent, snow water content, and evapotranspiration. The study area is Feather River Watershed (780,000 hectares), Northern California. The general approach involved a stepwise sequence of identification of the required information, sample design, measurement/estimation, and evaluation of results. All the relevent and available information types needed in the estimation process are being defined. These include Landsat, meteorological satellite, and aircraft imagery, topographic and geologic data, ground truth data, and climatic data from ground stations. A cost-effective multistage sampling approach was employed in quantification of all the required parameters. The physical and statistical models for both snow quantification and evapotranspiration estimation was developed. These models use the information obtained by aerial and ground data through appropriate statistical sampling design.

Korram, S.↗
NASA NTRS1982

Aspects of clear air turbulence severity forecasting and detection

Factors influencing the accuracy of the forecasts of incidences of clear air turbulence (CAT) are discussed, along with techniques for improved verification. Descriptive ranking terms for the intensity of CAT events, ranging from light to extreme, are developed, and meteorological parameters used for predictions are reviewed, including jetstream core location, vertical and horizontal wind shears, stable layers, tropopause height, trough speed, 500-mb vorticity, surface fronts, pressure centers and cyclogenesis, and wind speeds near mountain ridges. Methods of remote detection of CAT, particularly by using radiometry sensitive to the IR water vapor band, are noted to have had some success in detecting actual CAT events and decreasing false alarms. Statistical aspects of CAT encounter severity are discussed, including the establishment of confidence intervals for thresholds of detection of CATs of varying intensities.

Ehernberger, L. J.↗
NASA NTRS1987

Experiments with a three-dimensional statistical objective analysis scheme using FGGE data

A three-dimensional (3D), multivariate, statistical objective analysis scheme (referred to as optimum interpolation or OI) has been developed for use in numerical weather prediction studies with the FGGE data. Some novel aspects of the present scheme include: (1) a multivariate surface analysis over the oceans, which employs an Ekman balance instead of the usual geostrophic relationship, to model the pressure-wind error cross correlations, and (2) the capability to use an error correlation function which is geographically dependent. A series of 4-day data assimilation experiments are conducted to examine the importance of some of the key features of the OI in terms of their effects on forecast skill, as well as to compare the forecast skill using the OI with that utilizing a successive correction method (SCM) of analysis developed earlier. For the three cases examined, the forecast skill is found to be rather insensitive to varying the error correlation function geographically. However, significant differences are noted between forecasts from a two-dimensional (2D) version of the OI and those from the 3D OI, with the 3D OI forecasts exhibiting better forecast skill. The 3D OI forecasts are also more accurate than those from the SCM initial conditions. The 3D OI with the multivariate oceanic surface analysis was found to produce forecasts which were slightly more accurate, on the average, than a univariate version.

Baker, Wayman E.↗
NASA NTRS2010

Data Access Services that Make Remote Sensing Data Easier to Use

This slide presentation reviews some of the processes that NASA uses to make the remote sensing data easy to use over the World Wide Web. This work involves much research into data formats, geolocation structures and quality indicators, often to be followed by coding a preprocessing program. Only then are the data usable within the analysis tool of choice. The Goddard Earth Sciences Data and Information Services Center is deploying a variety of data access services that are designed to dramatically shorten the time consumed in the data preparation step. On-the-fly conversion to the standard network Common Data Form (netCDF) format with Climate-Forecast (CF) conventions imposes a standard coordinate system framework that makes data instantly readable through several tools, such as the Integrated Data Viewer, Gridded Analysis and Display System, Panoply and Ferret. A similar benefit is achieved by serving data through the Open Source Project for a Network Data Access Protocol (OPeNDAP), which also provides subsetting. The Data Quality Screening Service goes a step further in filtering out data points based on quality control flags, based on science team recommendations or user-specified criteria. Further still is the Giovanni online analysis system which goes beyond handling formatting and quality to provide visualization and basic statistics of the data. This general approach of automating the preparation steps has the important added benefit of enabling use of the data by non-human users (i.e., computer programs), which often make sub-optimal use of the available data due to the need to hard-code data preparation on the client side.

Lynnes, Christopher↗
NASA NTRS1981

Users manual for the US baseline corn and soybean segment classification procedure

A user's manual for the classification component of the FY-81 U.S. Corn and Soybean Pilot Experiment in the Foreign Commodity Production Forecasting Project of AgRISTARS is presented. This experiment is one of several major experiments in AgRISTARS designed to measure and advance the remote sensing technologies for cropland inventory. The classification procedure discussed is designed to produce segment proportion estimates for corn and soybeans in the U.S. Corn Belt (Iowa, Indiana, and Illinois) using LANDSAT data. The estimates are produced by an integrated Analyst/Machine procedure. The Analyst selects acquisitions, participates in stratification, and assigns crop labels to selected samples. In concert with the Analyst, the machine digitally preprocesses LANDSAT data to remove external effects, stratifies the data into field like units and into spectrally similar groups, statistically samples the data for Analyst labeling, and combines the labeled samples into a final estimate.

Horvath, R.↗
NASA NTRS1983

Satellite freeze forecast system: Executive summary

A satellite-based temperature monitoring and prediction system consisting of a computer controlled acquisition, processing, and display system and the ten automated weather stations called by that computer was developed and transferred to the national weather service. This satellite freeze forecasting system (SFFS) acquires satellite data from either one of two sources, surface data from 10 sites, displays the observed data in the form of color-coded thermal maps and in tables of automated weather station temperatures, computes predicted thermal maps when requested and displays such maps either automatically or manually, archives the data acquired, and makes comparisons with historical data. Except for the last function, SFFS handles these tasks in a highly automated fashion if the user so directs. The predicted thermal maps are the result of two models, one a physical energy budget of the soil and atmosphere interface and the other a statistical relationship between the sites at which the physical model predicts temperatures and each of the pixels of the satellite thermal map.

Martsolf, J. D.↗
NASA NTRS2009

Gaussian and Lognormal Models of Hurricane Gust Factors

A document describes a tool that predicts the likelihood of land-falling tropical storms and hurricanes exceeding specified peak speeds, given the mean wind speed at various heights of up to 500 feet (150 meters) above ground level. Empirical models to calculate mean and standard deviation of the gust factor as a function of height and mean wind speed were developed in Excel based on data from previous hurricanes. Separate models were developed for Gaussian and offset lognormal distributions for the gust factor. Rather than forecasting a single, specific peak wind speed, this tool provides a probability of exceeding a specified value. This probability is provided as a function of height, allowing it to be applied at a height appropriate for tall structures. The user inputs the mean wind speed, height, and operational threshold. The tool produces the probability from each model that the given threshold will be exceeded. This application does have its limits. They were tested only in tropical storm conditions associated with the periphery of hurricanes. Winds of similar speed produced by non-tropical system may have different turbulence dynamics and stability, which may change those winds statistical characteristics. These models were developed along the Central Florida seacoast, and their results may not accurately extrapolate to inland areas, or even to coastal sites that are different from those used to build the models. Although this tool cannot be generalized for use in different environments, its methodology could be applied to those locations to develop a similar tool tuned to local conditions.

Merceret, Frank↗
NASA NTRS1999

Impact of a Merged Precipitation Data on Global Soil Moisture Variability

Accurate soil moisture information has proved to be important to climate simulations and climate and weather forecasts. However, many difficulties exist that limit our understanding of soil moisture distribution and variability. One of them is the lack of accurate precipitation with appropriate spatial and temporal resolution. Precipitation as an input forcing to the land surface greatly influences soil moisture characteristics and variability. To improve precipitation data quality, an algorithm has been developed to generate a spatially and temporally continuous 3-hourly global precipitation data for the period of 1987 to present. This precipitation product is a combination of the precipitation from Special Sensor Microwave Imager (SSMI) with the Goddard Earth Observing System-1 Data Assimilation System (GEOS-1 DAS) employing a Physical-space Statistical Analysis System (PSAS). In this study we investigate the impact of this merged/analyzed precipitation data on the global soil moisture variability using an Off-line Land-surface GEOS Assimilation (OLGA) system. Two OLGA integrations starting from 1987 to 1993 are performed forced with the analyzed and GEOS-1 DAS precipitation respectively. We examine the spatial and temporal characteristics of soil moisture variability in response to the analyzed precipitation. The influence of this merged precipitation on the soil moisture variability and regional hydrological budget is estimated throughout the comparison with the results forced with the GEOS-1 DAS precipitation only. In the OLGA the sut@-grid scale horizontal heterogeneity is explicitly represented on the tile space. This provides a means to assess the role of the surface moisture heterogeneity in the interaction with the surface atmosphere and surface hydrological budget, and to validate OLGA results at tile space with in situ observation. ABRACOS (Anglo-Brazilian Amazonian Climate Observation Study), FIFE (First ISLSCP Field Experiment) I and HAPEX data will be used in the validation.

Yang, Runhua↗
NASA NTRS2017

Development of Gridded Innovations and Observations Supplement to MERRA-2

Atmospheric reanalysis have become an important source of data for weather and climate research, owing to the continuity of the data, but especially because of the multitude of observational data included (radiosondes, commercial aircraft, retrieved data products and radiances). However, the presence of assimilated observations can vary based on numerous factors, and so it is difficult or impossible for a researcher to say with any degree of certainty how many and what type of observations contributed to the reanalysis data they are using at any give point in time or space. For example, quality control, transmission interruptions, and station outages can occasionally affect data availability. While orbital paths can be known, drift in certain instruments and the large number of available instruments makes it challenging to know which satellite is observing any region at any point in the diurnal cycle. Furthermore, there is information from the statistics generated by the data assimilation that can help understand the model and the quality of the reanalysis. Typically, the assimilated observations and their innovations are in observation-space data formats and have not been made easily available to reanalysis users.A test data set has been developed to make the MERRA-2 assimilated observations available for rapid and general use, by simplifying the data format. The observations are binned to a grid similar as MERRA-2 and saved as netCDF. This data collection includes the mean and number of observations in the bin as well as its variance. The data will also include the innovations from the data assimilation, the forecast departure and the analysis increment, as well as bias correction (for satellite radiances). We refer to this proof-of-concept data as the MERRA-2 Gridded Innovations and Observations (GIO). In this paper, we present the data format and its strengths and limitations with some initial testing and validation of the methodology.

SSMI↗
NASA NTRS1990

Tropical Pacific moisture variability

The objectives are to describe synoptic scale variability of moisture over the tropical Pacific Ocean and the systems leading to this variability; implement satellite analysis procedures in support of this effort, and to incorporate additional satellite information into operational analysis forecast systems at the National Meteorological Center (NMC). Composite satellite radiance patterns describe features detectable well before the development of synoptic scale tropical plumes. These typical features were extracted from historical files of Tiros Operational Vertical Sounder (TOVS) radiance observations for a pair of tropical plumes which developed during January 1989. Signals were inserted into the NMC operational medium range forecast model and a suite of model integrations were conducted. Many of the 48 h model errors of the historical forecasts were eliminated by the inclusion of more complete satellite observations. Three studies in satellite radiance analysis progressed. An analysis which blended TOVS moisture channels, OLR observations and European Center for Medium Weather Forecasts (ECMWF) model analysis to generate fields of total precipitable water comparable to those estimated from Scanning Multichannel Microwave Radiometer (SMMR) mu-wave observations. This study demonstrated that a 10 y climatology of precipitable water over the oceans is feasible, using available infrared observations (OLR and TOVS) and model analysis (ECMWF, NMC or similar quality). The estimates are sensitive to model quality and the estimating model must be updated with operational model changes. Coe developed a set of tropical plume and ITCZ composites from TOVS observations, and from NMC and ECMWF analyses which had been passed through a radiative transfer model to simulate TOVS radiances. The composites have been completed as well as many statistical diagnostics of individual TOVS channels. Analysis of the computations is commencing. Chung has initiated a study of the differences between TOVS observed vapor structure during El Nino Southern Oscillation (ENSO) (1983) and non-ENSO (1984) years. Preliminary diagnosis demonstrates gross moisture changes between warm and cold sea surface temperature episodes.

Mcguirk, James P.↗
NASA NTRS2017

Comparison of Selected Weather Translation Products

Weather is a primary contributor to the air traffic delays within the National Airspace System (NAS). At present, it is the individual decision makers who use weather information and assess its operational impact in creating effective air traffic management solutions. As a result, the estimation of the impact of forecast weather and the quality of ATM response relies on the skill and experience level of the decision maker. FAA Weather-ATM working groups have developed a Weather-ATM integration framework that consists of weather collection, weather translation, ATM impact conversion and ATM decision support. Some weather translation measures have been developed for hypothetical operations such as decentralized free flight, whereas others are meant to be relevant in current operations. This paper does comparative study of two different weather translation products relevant in current operations and finds that these products have strong correlation with each other. Given inaccuracies in prediction of weather, these differences would not be expected to be of significance in statistical study of a large number of decisions made with a look-ahead time of two hours or more.

Kulkarni, Deepak↗
NASA NTRS2014

Limb Correction of RGB Composite Imagery for Improved Interpretation

RGB (Red‐Green‐Blue) imagery, created by integrating several spectral channels into one composite image, is currently used by the operational weather community to aid in quick, real‐time analysis of atmospheric processes. However, the limb effect - a result of an increasing optical path length of the absorbing atmosphere between the satellite and the earth as scan angle increases - interferes with the qualitative interpretation of RGB composites at large scan angles. It also makes the comparison of similar products from multiple satellite sensors difficult. Recent work has indicated that correcting for the limb effect in the basic channel imagery using simple statistical relationships greatly improves the utility of the derived RGB imagery. However, it is hypothesized that the limb correction coefficients vary with respect to latitude, season, cloud cover, and surface albedo. This poster will highlight an improved approach to the limb correction of RGB imagery using varying coefficients. The Joint Center for Satellite and Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) was used to simulate top of atmosphere brightness temperatures at varying scan angles for infrared channels corresponding to the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS), and Meteosat‐10 Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors. A subset of European Center for Medium‐Range Weather Forecasts (ECMWF) temperature, specific humidity, and ozone mixing ratio profiles from March 2013 through February 2014 were used as input to the CRTM. The simulated brightness temperatures were used to determine the best fit slope of the linear relationship between the natural log of the cosine of the scan angle and the difference of the simulated brightness temperature at nadir and on the limb. The correction coefficients were then analyzed for variability with respect to latitude, season, cloud cover, and surface type and used to produce improved limb corrected imagery. Applications of the results will be presented.

Elmer, Nicholas↗
NASA NTRS2018

Development of Gridded Innovations and Observations Supplement to MERRA-2

Atmospheric reanalysis have become an important source of data for weather and climate research, owing to the continuity of the data, but especially because of the multitude of observational data included (radiosondes, commercial aircraft, retrieved data products and radiances). However, the presence of assimilated observations can vary based on numerous factors, and so it is difficult or impossible for a researcher to say with any degree of certainty how many and what type of observations contributed to the reanalysis data they are using at any give point in time or space, or their contribution to the eventual analyzed fields. For example, quality control, transmission interruptions, and station outages can occasionally affect data availability. While orbital paths are known, drift in certain instruments, cloud clearing, and the large number of available instruments makes it challenging to know which satellite is observing any region at any point in the diurnal cycle. Furthermore, there is information from the statistics generated by the data assimilation that can help understand the model and the quality of the reanalysis. Typically, the assimilated observations and their innovations are saved in observation-space data formats and are not easily made available to reanalysis users.Here, we present an early version of a data set has been developed to make the MERRA-2 assimilated observations available for rapid and general use, by simplifying the data format. The observations are binned to a grid similar as MERRA-2 and saved as netCDF. This data collection includes the mean and number of observations in the bin as well as its variance. The data will also include the innovations from the data assimilation, the forecast departure and the analysis increment, as well as bias correction (for satellite radiances). In this paper, we present the data format (called MERRA-2 Gridded Innovations and Observations or GIO) and its strengths and limitations with some initial testing and validation of the methodology.

Bosilovich, Michael G.↗
NASA NTRS2016

Validation for Global Solar Wind Prediction Using Ulysses Comparison: Multiple Coronal and Heliospheric Models Installed at the Community Coordinated Modeling Center

The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and Heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, wehave quantitatively assessed the models capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs.The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.

Jian, L. K.↗
NASA NTRS2018

Flash Optical Energy from the Geostationary Lightning Mapper

The Geostationary Operational Environmental Satellite -16 (GOES-16) Geostationary Lightning Mapper (GLM) is evaluated for many months during the Post Launch Product Test (PLPT) phase in order to ensure that optimal products are available for both the operational forecasting and broader scientific research communities. The emphasis of the PLPT phase is to validate the GLM performance (i.e., lightning flash detection efficiency, geolocation and time-stamp accuracy) using an extensive network of independent ground-based, in-situ, and space-based reference lightning detection systems. However, another essential aspect of the PLPT phase is to obtain benchmarks of the GLM lightning optical amplitude, so that any long-term degradation in the nadir-staring GLM camera system can be realized and quantitatively assessed. This is accomplished in a straight-forward manner by collecting a very large sample of lightning flashes across many geographical regions in the GLM field-of-view so that statistically meaningful benchmarks of lightning optical amplitude (i.e., optical energy in units of femto-joules per flash) are obtained. The benchmarking is particularly important to follow-on studies that will attempt to incorporate the flash optical energy product into new derived products (e.g., energy-weighted lightning "jump" warning algorithms, and lightning nitrogen oxides production estimates).

Lightning↗