Global Precipitation Estimate Error Decomposition Analysis for 14 Passive Microwave Sensors
Explore the source record for details and available documents.
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
A description is presented of the results of a theoretical study to evaluate the remote sensing technique to determine integrated liquid water content. The considered technique uses 94 GHz passive microwave observations as applied to nonprecipitation clouds over the ocean. The study indicates that it is possible to infer the precipitable water of the atmosphere with a useful accuracy from a single 94 GHz nadir measurement and an estimated sea surface temperature. It is demonstrated that the integrated liquid water content of nonprecipitable clouds can be estimated with a relative accuracy of 20% to 30% depending on the atmospheric water vapor content from a microwave passive measurement at 94 GHz in the presence of clouds and in cloud free areas in the vicinity of the cloud in conjunction with information on the sea surface and cloud top temperatures.
Aspects of volume scattering and emission theory are discussed, taking into account a weakly scattering medium, the Born approximation, first-order renormalization, the radiative transfer method, and the matrix-doubling method. Other topics explored are related to scatterometers and probing systems, the passive microwave sensing of the atmosphere, the passive microwave sensing of the ocean, the passive microwave sensing of land, the active microwave sensing of land, and radar remote sensing applications. Attention is given to inversion techniques, atmospheric attenuation and emission, a temperature profile retrieval from ground-based observations, mapping rainfall rates, the apparent temperature of the sea, the emission behavior of bare soil surfaces, the emission behavior of vegetation canopies, the emission behavior of snow, wind-vector radar scatterometry, radar measurements of sea ice, and the back-scattering behavior of cultural vegetation canopies.
Detection of metal objects on or near the Earth's surface was investigated using existing, passive, microwave sensors operating from Earth orbit. The range equations are derived from basic microwave principles and theories and the expressions are given explicitly to estimate the signal to noise ratio for detecting metal targets operating as bistatic scatterers. Actual measurements are made on a range of metal objects observed from orbit using existing passive microwave receiving systems. The details of the measurements and the results are tabulated and discussed. The advantages of a passive microwave sensor as it is applied to surveillance of metal objects as viewed from aerial platforms or from orbit, are examined.
Two long records of melt onset (MO) on Arctic sea ice from passive microwave brightness temperatures (Tbs) obtained by a series of satellite-borne instruments are compared. The Passive Microwave (PMW) method and Advanced Horizontal Range Algorithm (AHRA) detect the increase in emissivity that occurs when liquid water develops around snow grains at the onset of early melting on sea ice. The timing of MO on Arctic sea ice influences the amount of solar radiation absorbed by the ice-ocean system throughout the melt season by reducing surface albedos in the early spring. This work presents a thorough comparison of these two methods for the time series of MO dates from 1979through 2012. The methods are first compared using the published data as a baseline comparison of the publically available data products. A second comparison is performed on adjusted MO dates we produced to remove known differences in inter-sensor calibration of Tbs and masking techniques used to develop the original MO date products. These adjustments result in a more consistent set of input Tbs for the algorithms. Tests of significance indicate that the trends in the time series of annual mean MO dates for the PMW and AHRA are statistically different for the majority of the Arctic Ocean including the Laptev, E. Siberian, Chukchi, Beaufort, and central Arctic regions with mean differences as large as 38.3 days in the Barents Sea. Trend agreement improves for our more consistent MO dates for nearly all regions. Mean differences remain large, primarily due to differing sensitivity of in-algorithm thresholds and larger uncertainties in thin-ice regions.
Progress by investigators at Georgia Tech in defining the requirements for large space antennas for passive microwave Earth imaging systems is reviewed. In order to determine antenna constraints (e.g., the aperture size, illumination taper, and gain uncertainty limits) necessary for the retrieval of geophysical parameters (e.g., rain rate) with adequate spatial resolution and accuracy, a numerical simulation of the passive microwave observation and retrieval process is being developed. Due to the small spatial scale of precipitation and the nonlinear relationships between precipitation parameters (e.g., rain rate, water density profile) and observed brightness temperatures, the retrieval of precipitation parameters are of primary interest in the simulation studies. Major components of the simulation are described as well as progress and plans for completion. The overall goal of providing quantitative assessments of the accuracy of candidate geosynchronous and low-Earth orbiting imaging systems will continue under a separate grant.
Knowledge of global precipitation means, patterns and variations is essential for understanding the global water cycle. The observation-based global analysis of the Global Precipitation Climatology Project (GPCP) has been a key input to many such studies, including those related to means of the global (and regional) water and energy cycles. A new version (Version 3.1) of the GPCP monthly analysis is now available (1983-2019), with finer spatial resolution (0.5º latitude/longitude), updated satellite algorithms, latest gauge analysis over land from the Global Precipitation Climatology Center (GPCC), and with ocean climatologies adjusted using information from the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission and CloudSat. The presentation will give key findings from the new analysis, compare with the previous version, link to studies of the water cycle and compare to CMIP6 climate model results. The GPCP Monthly V3.1 analysis uses a Tropical Composite Climatology (TCC) using 22 years (1998-2019) of TRMM and GPM-based surface precipitation estimates from passive microwave, radar and combined passive microwave and radar observations to adjust the long-term mean values in the tropics over ocean. At higher latitudes over ocean climatological values of merged CloudSat and GPM combined passive microwave and radar surface precipitation estimates are used. The result of the new algorithms and procedures is an ocean mean value 60º N to 60º S of 3.2 mm/d, an increase of 7% from the older V2.3. Over land the gauge analysis (from GPCC in Germany), combined with satellite estimates results in a small decrease (~ 0.5%) from the previous version, giving a total global precipitation mean of 2.81 mm/d for V3.1, an increase of 4.5%. Variations from inter-annual to trend scales over most of the ocean are driven by the Colorado State University (CSU) Goddard Profiling (GPROF) algorithm applied to SSMI/SSMIS satellite data. The new GPCP version retains a near zero trend of global precipitation, with significant positive trends in the deep tropics along the Pacific ITCZ and elsewhere, countered by middle latitude decreases, very similar to the previous version of the GPCP Monthly analysis. The pattern of trends is similar to that of AMIP climate model results, driven by observed SSTs for the period in question, but very different from results for “free-running” CMIP historical ensembles, likely due in part to the relatively short comparison period and effects of inter-decadal variations in the GPCP and AMIP results that are not in the “history” models. Interannual variations are also nearly the same in the new version, but with finer detail, although ENSO variations over the ocean have slightly larger amplitude than before, an effect likely related to the change in ocean satellite algorithm used in Version 3. For precipitation intensity (percentiles) at the monthly scale, especially in the tropics, GPCP shows a positive trend for the upper one third of the percentiles (Pct ≥ 70th) and a much weaker positive trend for the lowest percentiles (Pct ≤ 10th), while negative trends appear for the middle one-half percentiles (20th-65th). AMIP results agree with those from the GPCP in terms of the sign of the changes/trends for high and intermediate percentiles. The CMIP historical results also agree in the sign of the trends, but the trends are weaker. Comparisons with other types of CMIP historical simulations including the GHG-only, aerosol-only, and nature-only runs suggest that the observed changes/trends in precipitation amount and intensity during the GPCP period are dominated by a combination of the effects of the Pacific Decadal Oscillation (PDO) and anthropogenic GHG-related surface warming.
Passive microwave sensing is sensitive to a wide variety of geophysical parameters in the atmosphere, oceans, cryosphere, and on land. For example, spaceborne passive microwave atmospheric sounders provide the highest-impact observations for state-of-the-art weather forecast models used by meteorological agencies around the world. The ability of passive microwave sensors (radiometers) to observe day or night through cloud cover and other obstacles (wavelength dependent, of course) partially compensates for their sometimes large footprint size compared to other sensor types. Examples will be presented of radiometers deployed on the ground, on aircraft, and on satellites. Each of these environments presents different challenges in obtaining accurate observations. And while many standard radiometer designs and techniques have been developed, every user re-discovers that even the "standard" approach requires careful attention in order to achieve full potential.
The three components of microwave remote sensing (sensor-scene interaction, sensor design, and measurement techniques), and the applications to geoscience are examined. The history of active and passive microwave sensing is reviewed, along with fundamental principles of electromagnetic wave propagation, antennas, and microwave interaction with atmospheric constituents. Radiometric concepts are reviewed, particularly for measurement problems for atmospheric and terrestrial sources of natural radiation. Particular attention is given to the emission by atmospheric gases, clouds, and rain as described by the radiative transfer function. Finally, the operation and performance characteristics of radiometer receivers are discussed, particularly for measurement precision, calibration techniques, and imaging considerations.
Past studies of passive microwave measurements of precipitating systems have yielded broad empirical relationships between hydrometeors and microwave transmission. In general, these relationships fall into two categories of passive microwave precipitation retrievals rely upon the observed effect of liquid precipitation to increase the brightness temperature of a radiometrically cold background such as an ocean surface. A scattering-based method is based upon the effect that frozen hydrometeors tend to decrease the brightness temperature of a radiometrically warm background such as land. One step toward developing quantitative brightness temperature-rain rate relationships is the recent construction of a new aircraft instrument sponsored by National Aeronautics and Space Administration/Marshall Space Flight Center (NASA/MSFC). This instrument is the Advanced Microwave Precipitation Radiometer (AMPR) designed and built by Georgia Tech Research Institute to fly aboard high altitude research aircraft such as the NASA ER-2. The AMPR and its accompanying data acquisition system are mounted in the Q-bay compartment of the NASA ER-2.
Active and passive microwave data collected during the 1984 summer Marginal Ice Zone Experiment in the Fram Strait (MIZEX 84) are used to compare ice concentration estimates derived from synthetic aperture radar (SAR) data to those obtained from passive microwave imagery at several frequencies. The comparison is carried out to evaluate SAR performance against the more established passive microwave technique, and to investigate discrepancies in terms of how ice surface conditions, imaging geometry, and choice of algorithm parameters affect each sensor. Active and passive estimates of ice concentration agree on average to within 12%. Estimates from the multichannel passive microwave data show best agreement with the SAR estimates because the multichannel algorithm effectively accounts for the range in ice floe brightness temperatures observed in the MIZ.
Active and passive microwave and physical properties of Arctic sea ice in the marginal ice zone were measured during the summer. Results of an intercomparison of data acquired by an aircraft synthetic aperture radar, a passive microwave imager and a helicopter-mounted scatterometer indicate that early-to-mid summer sea ice microwave signatures are dominated by snowpack characteristics. Measurements show that the greatest contrast between thin first-year and multiyear sea ice occurs when operating actively between 5 and 10 GHz. Significant information about the state of melt of snow and ice is contained in the active and passive microwave signatures.
We have developed an algorithm that retrieves wind speed under rain using C-hand and X-band channels of passive microwave satellite radiometers. The spectral difference of the brightness temperature signals due to wind or rain allows to find channel combinations that are sufficiently sensitive to wind speed but little or not sensitive to rain. We &ve trained a statistical algorithm that applies under hurricane conditions and is able to measure wind speeds in hurricanes to an estimated accuracy of about 2 m/s. We have also developed a global algorithm, that is less accurate but can be applied under all conditions. Its estimated accuracy is between 2 and 5 mls, depending on wind speed and rain rate. We also extend the wind speed region in our model for the wind induced sea surface emissivity from currently 20 m/s to 40 mls. The data indicate that the signal starts to saturate above 30 mls. Finally, we make an assessment of the performance of wind direction retrievals from polarimetric radiometers as function of wind speed and rain rate
Nine gridded Northern Hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Intercomparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS-2; the European Centre for Medium-Range Weather Forecasts (ECMWF) interim land surface reanalysis – ERA-Interim/Land and ERA5; the NASA Modern-Era Retrospective Analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2); the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) stand-alone passive microwave retrievals (NASA AMSR-E SWE versions 1.0 and 2.0) which do not utilize surface snow observations. Evaluation included validation against independent snow course measurements from Russia, Finland, and Canada and product intercomparison through the calculation of spatial and temporal correlations in SWE anomalies. The stand-alone passive microwave SWE products (AMSR-E v1.0 and v2.0 SWE) exhibit low spatial and temporal correlations to other products and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides performance comparable to the reanalysis-based products; RMSE over Finland and Russia for all but the AMSR-E products is ∼50 mm or less, with the exception of ERA-Interim/Land over Russia. Using a seven-dataset ensemble that excluded the stand-alone passive microwave products reduced the RMSE by 10 mm (20 %) and increased the correlation from 0.67 to 0.78 compared to any individual product. The overall performance of the best multiproduct combinations is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.
The potential of space systems and technology for detecting and monitoring ocean oil spills and waste pollution was assessed as well as the impact of this application on communication and data handling systems. Agencies charged with responsibilities in this area were identified and their measurement requirements were ascertained in order to determine the spatial resolution needed to characterize operational and accidental discharges. Microwave and optical sensors and sensing techniques were evaluated as candidate system elements. Capabilities are described for the following: synthetic aperture radar, microwave scatterometer, passive microwave radiometer, microwave altimeter, electro-optical sensors currently used in airborne detection, existing space-based optical sensors, the thematic mapper, and the pointable optical linear array.
A concise description of the principles and applications of several selected instruments that have been utilized most frequently in remote sensing of the ocean from satellites is presented. Emphasis is placed on the current progress in oceanographic applications and the outlook of the instruments in future oceanographic satellite missions is discussed. The instruments under discussion are placed into three groups: active microwave sensors, passive ocean color and infrared sensors, and passive microwave sensors.