Geostationary Operational Satellite Research Articles

Automatic mapping of 500 m daily open water body fraction in the American continent using GOES-16 ABI imagery

Timely and large-area monitoring of terrestrial water bodies using remote sensing data is essential to protect water resource security for humans and ecosystems. Although efforts have been made to monitor inter-monthly water bodies at the regional, continental and global scales, surface open water bodies may experience rapid changes within a short duration. At the same time, the data gaps caused by the long revisit frequencies of satellite sensor imaging systems and the extremely cloudy and rainy weather in certain areas mean that a suitable operational method for recording such rapid changes over large areas does not yet exist. The recently launched operational geostationary satellite sensor GOES-16 ABI can provide multi-scale optical images every 10 min in the Americas, and it has great potential to provide a new solution for timely open surface water monitoring. In this research, a framework based on a multi-scale non-linear mixture model (NLMM) was proposed for the automatic estimation of 500 m daily open water body fraction maps across the full extent of the American continent from GOES-16 ABI imagery. Specifically, an exterior spectral library was created firstly from many pairs of training GOES-16 ABI images and 500 m water body fraction maps derived from Landsat images, and an inherited interior pure spectral library was then derived from a pair of the testing GOES-16 ABI image and the corresponding pure water and non-water (i.e., vegetation and soil/rock substrate) fractions extracted from the monthly water recurrence dataset. Furthermore, a synthetic interior mixed spectral library was built finally based on the linear and non-linear mixtures of randomly selected pure endmembers of water and non-water (i.e., vegetation and soil/rock substrate) in the testing image. All the exterior and interior pure and synthetic mixed spectral libraries were employed jointly in the NLMM machine learning method for estimating daily water body fractions with GOES-16 ABI images. Experiments show that the integration of all three different spectral libraries produced 500 m open water body fraction maps with the greatest accuracies. Compared to traditional linear spectral unmixing and a water index thresholding method, the proposed method produced more accurate results both quantitatively and visually. Moreover, the proposed method can produce time-series daily 500 m open water body fraction maps with minimal cloud coverage for the full extent of the Americas by combining dense GOES-16 ABI images at half-hour intervals, opening up great possibilities for near real-time rapid open water body changes monitoring, such as flooding and drought, over large areas, even with a high frequency of cloud cover.

Nowcasting Applications of Geostationary Satellite Hourly Surface PM2.5 Data.

The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 μm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States (US) were derived from NOAA's operational geostationary satellites Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of -21.4% and -15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 μg/m3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the US, but with pronounced differences in the western US due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (> 35 μg/m3), the hourly estimates of PM2.5 used in Nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality ten times more than standard ground observations (1.8 vs. 0.17 million people per hour).

A Demonstration of Three-Satellite Stereo Winds

Stereo tracking of clouds from multiple satellites permits the simultaneous retrieval of an atmospheric motion vector (“wind”) and its height in the atmosphere. The direct measurement of height is a major advantage of stereo methods over observations made from a single satellite where the height must be inferred from infrared brightness temperatures. A pair of operational geostationary satellites over the Americas provides stereo coverage where their two fields of view intersect. Stereo coverage can be extended to nearly a full hemisphere with a third satellite. We demonstrate this configuration with the operational GOES-R constellation of GOES-16 (east) and GOES-17 (west) augmented by GOES-18 in its central test slot and use the 500-m resolution Advanced Baseline Imager Band 2. We examine the consistency of the pairwise products created from GOES-18 and -16 versus GOES-18 and -17 and create a fused triple-GOES product that spans nearly the full hemisphere seen from GOES-18. We also examine the retrieval of ground points observed under clear skies and compare their retrievals to zero speed and known terrain heights. The results are compatible with a wind accuracy about 0.1 m/s with height assignment uncertainty of 175 m.

Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.

Emerging satellite observations for diurnal cycling of ecosystem processes.

Diurnal cycling of plant carbon uptake and water use, and their responses to water and heat stresses, provide direct insight into assessing ecosystem productivity, agricultural production and management practices, carbon and water cycles, and feedbacks to the climate. Temperature, light, atmospheric water demand, soil moisture and leaf water potential vary over the course of the day, leading to diurnal variations in stomatal conductance, photosynthesis and transpiration. Earth observations from polar-orbiting satellites are incapable of studying these diurnal variations. Here, we review the emerging satellite observations that have the potential for studying how plant functioning and ecosystem processes vary over the course of the diurnal cycle. The recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Orbiting Carbon Observatory-3 (OCO-3) provide land surface temperature, evapotranspiration (ET), gross primary production (GPP) and solar-induced chlorophyll fluorescence data at different times of day. New generation operational geostationary satellites such as Himawari-8 and the GOES-R series can provide continuous, high-frequency data of land surface temperature, solar radiation, GPP and ET. Future satellite missions such as GeoCarb, TEMPO and Sentinel-4 are also planned to have diurnal sampling capability of solar-induced chlorophyll fluorescence. We explore the unprecedented opportunities for characterizing and understanding how GPP, ET and water use efficiency vary over the course of the day in response to temperature and water stresses, and management practices. We also envision that these emerging observations will revolutionize studies of plant functioning and ecosystem processes in the context of climate change and that these observations and findings can inform agricultural and forest management and lead to improvements in Earth system models and climate projections.

Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

Abstract. An ability to accurately detect convective regions is essential for initializing models for short-term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high-temporal-resolution data are mostly available over land, and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface or cloud growth that occurs over periods of 30 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-temporal-resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses a rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

Assessment of Extreme Diurnal Warming in Operational Geosynchronous Satellite Sea Surface Temperature Products

We evaluate the reliability and basic characteristics of observations of extreme DW events from current operational geostationary satellite sea surface temperature (SST) products through examination of three months of diurnal warming (DW) estimates derived by different methodologies from the Spinning Enhanced Visible and Infrared Imager on Meteosat-11, Advanced Himawari Imager on Himawari-8, and Advanced Baseline Imager on the Geostationary Operational Environmental Satellite (GOES)-16. This work primarily focuses on the following research questions: (1) Can these operational SST products accurately characterize extreme DW events? (2) What are the amplitudes and frequencies of these events? To answer these, we compute distributions of DW and DW exceedance and compare them amongst the different methods and geostationary sensors. Examination of the DW estimates demonstrates several challenges in accurately deriving distributions of the DW amplitude, particularly associated with estimating the “foundation” temperature and uncertainties in cloud screening. Overall, the results suggest that current geostationary sensors can reliably assess extreme DW, but the estimates are sensitive to the computational methods applied. We thus suggest careful processing/screening of the SST retrievals. We find a value of 3 K, corresponding to the 99th percentile, provides a potential practical threshold for extreme warming, but events of at least 6 K were reliably observed. Warming in excess of 6 K occurred somewhere an average of 47% of the time, and its probability at a given location was of O(10−6).

Spatiotemporal Assessments on the Satellite‐Based Precipitation Products From Fengyun and GPM Over the Yunnan‐Kweichow Plateau, China

AbstractHigh‐quality precipitation data are vital for hydrological applications and climate change research. In this study, we evaluated two satellite‐based precipitation products from Chinese Fengyun (FY) 2G (G is one of the third batch of operational geostationary satellites in the Chinese FY‐2 series) and the Global Precipitation Measurement (GPM) against the rain gauge‐based precipitation data, respectively, over the Yunnan‐Kweichow Plateau, China, at different temporal scales (e.g., monthly, daily, and hourly). And the main findings of this study were as follows: (1) FY2G Quantitative Precipitation Estimation (QPE) and Integrated Multisatellite Retrievals for GPM (IMERG) shared similar spatial precipitation patterns with those of rain gauge data, while the FY2G QPE general underestimated the precipitation (bias, −30%–0%), and IMERG obviously overestimated the precipitation (bias, 0%–60%) at monthly scale over the Yunnan‐Kweichow Plateau; (2) the FY2G QPE correlated significantly better (Correlation coefficient, CC, ~0.80) than IMERG (CC ~0.20) against rain gauge data at daily scale, meanwhile, the average daily bias value of IMERG (~30%) was around 6 times larger than that of FY2G QPE (~5%); (3) The FY2G QPE (CC ~0.7, bias ~ − 8%, root‐mean‐square error ~2.0 mm/hr, mean absolute error ~ 0.6 mm/hr) also generally outperformed the IMERG (CC ~0.1, bias ~13%, root‐mean‐square error ~3.0 mm/hr, mean absolute error ~ 1.0 mm/hr), at hourly scale; (4) The FY2G QPE significantly underestimated the precipitation in the period between 16:00 and 20:00 with the probability of detection (POD) decreased from 0.8 to 0.5, while the IMERG significantly overestimated the precipitation in the period between 4:00 and 12:00, at diurnal scale; and (5) one of the reasons resulting the significant overestimations of IMERG might due to its weak abilities in detecting the precipitation events with POD ~0.2 and false alarm ratio (FAR) ~0.7 at diurnal scale, which performed worse than those of the FY2G QPE (POD ~0.8 and FAR ~0.4). These findings would provide valuable recommendations for using these satellite‐based precipitation products in various application fields, such as hydrology, agriculture, and meteorology over the YKP, and also for improving the algorithms of GPM.

Earth Science Research From Operational Geostationary Satellite Systems (NASA)

Federal Grants & ContractsVolume 43, Issue 21 p. 5-5 Grant Alerts Earth Science Research From Operational Geostationary Satellite Systems (NASA) First published: 01 October 2019 https://doi.org/10.1002/fgc.30682Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume43, Issue213 October 2019Pages 5-5 RelatedInformation

Distribution and Variability of Satellite‐Derived Signals of Isolated Convection Initiation Events Over Central Eastern China

AbstractThis study combined measurements from the Chinese operational geostationary satellite Fengyun‐2E (FY‐2E) and ground‐based weather radars to conduct a statistical survey of isolated convection initiation (CI) over central eastern China (CEC). The convective environment in CEC is modulated by the complex topography and monsoon climate. From May to August 2010, a total of 1,630 isolated CI signals were derived from FY‐2E using a semiautomated method. The formation of these satellite‐derived CI signals peaks in the early afternoon and occurs with high frequency in areas with remarkable terrain inhomogeneity (e.g., mountain, water, and mountain‐water areas). The high signal frequency areas shift from northwest CEC (dry, high altitude) in early summer to southeast CEC (humid, low altitude) in midsummer along with an increasing monthly mean frequency. The satellite‐derived CI signals tend to have longer lead times (the time difference between satellite‐derived signal formation and radar‐based CI) in the late morning and afternoon than in the early morning and night. During the early morning and night, the distinction between cloud top signatures and background terrestrial radiation becomes less apparent, resulting in delayed identification of the signals and thus short and even negative lead times. A decline in the lead time is observed from May to August, likely due to the increasing cloud growth rate and warm‐rain processes. Results show increasing lead times with increasing landscape elevation, likely due to more warm‐rain processes over the coastal sea and plain, along with a decreasing cloud growth rate from hill and mountain to the plateau.

The capability of accuracy improvement in point positioning using the combination of GPS and SBAS satellites

The geostationary satellite of SBAS transmits not only corrections but also provides the code C/A on the L1 frequency. On which SBAS geostationary satellites like GPS satellites provide pseudo-ranges measurement C1. The features of this measurement is that it is always permanent because geostationary satellites do not move. This will be an additional source of pseudo-range measurement for the GPS system. With the current constellation of all SBAS systems, the number of operational geostationary satellites in orbit around the equator has risen to 12 (the number will increase in the future) and be distributed to give global coverage. The objective of this study is to present several surveys to evaluate accuracy improvement capability in point positioning when processing mixed measurements from the geostationary satellites in the SBAS and GPS. The results show that PDOP can be improved from 1.2 to 1.7 times when mixed processing.

Bias and observation error characterization of the Fengyun-2F Stretched Visible and Infrared Spin Scan Radiometer with the aim of its radiance assimilation

ABSTRACTRecent studies start to explore the potential benefits of directly assimilating the radiance from the infrared imagers onboard geostationary satellites to the short-range numerical weather prediction both on the synoptic- and meso-scales, where accurate quantification of the bias and observation error arising from the numerical model and the instrument is a key component in such experiments. This paper investigates both the bias and the observation errors of the Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) onboard Chinese operational geostationary satellite Fengyun-2F, as the first step towards its clear-sky radiance assimilation. In this study, 774,530 observations that passed a five-step quality control procedure during the period of 1–7 July in 2014 are collected to investigate the mentioned two quantities while the synthetic simulated clear radiance is calculated through a numerical weather prediction model and a radiative transfer model. Persistent warm biases are found for FY-2 F S-VISSR channel 3, 4 and 5 during day and night, and they are possibly due to the uncertainty from the instrument calibration process. The observation errors of S-VISSR infrared channels vary with the surface types, generally with larger values over land than over ocean, which could partially be explained by the variation of the land types over land. Besides, whether these biases change with viewing geometry or evolve with time is also examined in our study. Biases from the three surface channels exhibit complex dependence on the scan position and evolve with time, which encourages incorporating both the scan position and temporal corrections into the S-VISSR bias correction scheme in the data assimilation system.

A preliminary study on dead geostationary satellite removal

The collision between satellites IRIDIUM 33 and COSMOS 2251 indicated that the clash of two on-orbit satellites was becoming an inevitable reality. Our calculation with the two-line orbit element by NORAD showed that some two geostationary satellites had approached very close in July 2009. Therefore, more attention should be given to avoid such collisions. This paper analyzes the orbital long-term variation of a dead satellite drifting in the geostationary orbit. Also, the negative effects posed by dead satellites upon the on-orbit operational geostationary satellites are studied. Then the paper proposes a novel idea to launch a satellite sweeper whose purpose is to collect the on-orbit dead satellites and help them de-orbit to a “graveyard”. The satellite sweeper consists of a parent satellite and a child satellite. The child satellite collects a dead satellite and transfers it to a higher orbit. The parent satellite stationed in the geostationary orbit is in charge of refueling the child satellite. The strategy of maneuver and rendezvous is presented and a series of formulas are derived. The analysis results show that our method to clean the geostationary orbital zone is practical and fuel-saving. With the help of just a few satellite sweepers, we can gain a clean environment of geostationary orbit environment again.

NOAA's Sea Surface Temperature Products From Operational Geostationary Satellites

NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) has generated sea surface temperature (SST) products from Geostationary Operational Environmental Satellite (GOES)-East (E) and GOES-West (W) on an operational basis since December 2000. Since that time, a process of continual development has produced steady improvements in product accuracy Recent improvements extended the capability to permit generation of operational SST retrievals from the Japanese Multifunction Transport Satellite (MTSAT)-IR and the European Meteosat Second Generation (MSG) satellite, thereby extending spatial coverage. The four geostationary satellites (at longitudes of 75°W, 135°W, 140°E, and 0°) provide high temporal SST retrievals for most of the tropics and midlatitudes, with the exception of a region between ~60° and ~80°E. Because of ongoing development, the quality of these retrievals now approaches that of SST products from the polar-orbiting Advanced Very High Resolution Radiometer (AVHRR). These products from GOES provide hourly regional imagery, 3-hourly hemispheric imagery, 24-h merged composites, a GOES SST level 2 preprocessed product every ½ h for each hemisphere, and a match-up data file for each product. The MTSAT and the MSG products include hourly, 3-hourly, and 24-h merged composites. These products provide the user community with a reliable source of SST observations, with improved accuracy and increased coverage in important oceanographic, meteorological, and climatic regions.

Air Quality Forecast Verification Using Satellite Data

Abstract NOAA’s operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service developmental (research mode) particulate matter (PM2.5) predictions tested during the summer 2004 International Consortium for Atmospheric Research on Transport and Transformation/New England Air Quality Study (ICARTT/NEAQS) field campaign. The forecast period included long-range transport of smoke from fires burning in Canada and Alaska and a regional-scale sulfate event over the Gulf of Mexico and the eastern United States. Over the 30-day time period for which daytime hourly forecasts were compared with observations, the categorical (exceedance defined as AOD > 0.55) forecast accuracy was between 0% and 20%. Hourly normalized mean bias (forecasts − observations) ranged between −50% and +50% with forecasts being positively biased when observed AODs were small and negatively biased when observed AODs were high. Normalized mean errors are between 50% and 100% with the errors on the lower end during the 18–22 July 2004 time period when a regional-scale sulfate event occurred. Spatially, the errors are small over the regions where sulfate plumes were present. The correlation coefficient also showed similar features (spatially and temporally) with a peak value of ∼0.6 during the 18–22 July 2004 time period. The dominance of long-range transport of smoke into the United States during the summer of 2004, neglected in the model predictions, skewed the model forecast performance. Enhanced accuracy and reduced normalized mean errors during the time period when a sulfate event prevailed show that the forecast system has skill in predicting PM2.5 associated with urban/industrial pollution events.

Daily Rainfall for the Indian Monsoon Region from Merged Satellite and Rain Gauge Values: Large-Scale Analysis from Real-Time Data

Abstract A system for objectively producing daily large-scale analysis of rainfall for the Indian region has been developed and tested by using only available real-time rain gauge data and quantitative precipitation estimates from INSAT-1D IR data. The system uses a successive correction method to produce the analysis on a regular latitude–longitude grid. Quantitative precipitation estimates from the Indian National Satellite System (INSAT) operational geostationary satellite, INSAT-1D, IR data are used as the initial guess in the objective analysis method. Accumulated 24-h (daily) rainfall analyses are prepared each day by merging satellite and rain gauge data. The characteristics of the output from this analysis system have been examined by comparing the accumulated monthly observed rainfall with other available independent widely used datasets from the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) analyses. The monthly data prepare...

Impact of a consistent height reassignment method for INSAT derived cloud motion vectors

¶Cloud Motion Vector wind (CMV) data is one of the major products derived from current day operational geostationary satellites and continues to be a useful source of information for global data assimilation systems. The CMV fields result basically from the apparent cloud motion estimated through a sequence of very high resolution radiometer (VHRR) images and the corresponding cloud level is inferred from observed brightness temperatures (BT) in the infrared window channel. The main problem in CMV production is associated with the assignment of correct height to the cloud tracked motions in view of the known limitations associated with the cloud emissivity characteristics and estimation of BTs. Despite these inherent uncertainties satellite derived CMVs are widely used to augment the conventional upper air wind data especially over the oceanic regions. In this study, an attempt is made to improve the quality of INSAT derived CMVs through an appropriate height re-assignment by employing first guess fields (short range forecasts) of a global forecast model.

Satellite meteorology: an introduction

Introduction: History of Satellite Meteorology. Scope of The Book. Orbits and Navigation: Newton's Laws. Keplerian Orbits. Orbit Perturbations. Meteorological Satellite Orbits. Satellite Positioning, Tracking and Navigation. Space-Time Sampling. Launch Vehicles and Profiles. Radiative Transfer: Basic Quantities. Blackbody Radiation. The Radiative Transfer Equation. Gaseous Absorption. Scattering. Surface Reflection. Solar Radiation. Meteorological SatelliteInstrumentation: Operational Polar-Orbiting Satellites. Operational Geostationary Satellites. Other Satellite Instruments. Satellite Data Archives. Image Interpretation: Satellite Imagery. Spectral Properties. Image Enhancement Techniques. Geolocation and Calibration. Atmospheric and Surface Phenomena. A Final Note.Temperature and Trace Gases: Sounding Theory. Retrieval Methods. Operational Retrievals. Limb Sounding Retrievals. Ozone and Other Gases. The Split-Window Technique. Winds: Cloud and Vapor Tracking. Winds from Soundings. Ocean Surface Winds. Doppler Wind Measurements. Clouds and Aerosols: Clouds from Sounders. Clouds from Imagers. Clouds from Microwave Radiometry. Stratospheric Aerosols. Tropospheric Aerosols. Precipitation: Visible and Infrared Techniques. Passive Microwave Techniques. Radar. Severe Thunderstorms. Earth Radiation Budget: The Solar Constant. Top of the Atmosphere Radiation Budget. Surface Radiation Budget. The Future: NOAA K, L, M.Mission to Planet Earth. Other Possibilities. A Final Comment. Appendixes: List of Meteorological Satellites. Abbreviations. Principal Symbols. Systeme International Units. Physical Constants. Subject Index.

A Comparison of Several Techniques to Assign Heights to Cloud Tracers

Experimental results are presented which suggest that the water-vapor technique of radiance measurement is a viable alternative to the CO2 technique for inferring the height of semitransparent cloud elements. Future environmental satellites will rely on H2O-derived cloud-height assignments in the wind-field determinations with the next operational geostationary satellite. On a given day, the heights from the H2O and CO2 approaches compare to within 60-110 hPa rms.

The global precipitation climatology project

The Global Precipitation Climatology Project of the World Climate Research Programme is an effort to use a variety of satellite estimates of rainfall together with station observations to derive a 10-year climatology of monthly rainfall for areas of 2.5° latitude/longitude. The Project will use rainfall estimates from both geostationary and polar orbiting satellites along with station observations to perform the global analysis. In the tropics, the primary data source used will be geostationary IR imagery, while passive microwave radiometric observations will be used over the extratropical oceans. A mix of data sources will be used over the continents. In this paper, we describe the various components of the Project, describe the process by which the operational geostationary satellite data are merged and the rainfall estimates derived, and show examples of tropical rainfall variability during 1987.