scholarly journals Special Sensor Microwave Imager (SSM/I) Intersensor Calibration Using a Simultaneous Conical Overpass Technique

2011 ◽  
Vol 50 (1) ◽  
pp. 77-95 ◽  
Author(s):  
Song Yang ◽  
Fuzhong Weng ◽  
Banghua Yan ◽  
Ninghai Sun ◽  
Mitch Goldberg
Keyword(s):  
Precipitable Water ◽  
Calibration Procedure ◽  
Environmental Data ◽  
Sensor Data ◽  
Global Ocean ◽  
Monthly Precipitation ◽  
Tropical Ocean ◽  
Climate Trend ◽  
Surface Precipitation ◽  
Microwave Imager

Abstract A new intersensor calibration scheme is developed for the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) to correct its scan-angle-dependent bias, the radar calibration beacon interference on the F-15 satellite, and other intersensor biases. The intersensor bias is characterized by the simultaneous overpass measurements with the F-13 SSM/I as a reference. This sensor data record (SDR) intersensor calibration procedure is routinely running at the National Oceanic and Atmospheric Administration and is now used for reprocessing all SSM/I environmental data records (EDR), including total precipitable water (TPW) and surface precipitation. Results show that this scheme improves the consistency of the monthly SDR’s time series from different SSM/I sensors. Relative to the matched rain products from the Tropical Rainfall Measuring Mission, the bias of SSM/I monthly precipitation is reduced by 12% after intersensor calibration. TPW biases between sensors are reduced by 75% over the global ocean and 20% over the tropical ocean, respectively. The intersensor calibration reduces biases by 20.6%, 15.7%, and 6.5% for oceanic, land, and global precipitation, respectively. The TPW climate trend is 1.59% decade−1 (or 0.34 mm decade−1) for the global ocean and 1.39% decade−1 (or 0.63 mm decade−1) for the tropical ocean, indicating related trends decrease of 38% and 54%, respectively, from the uncalibrated SDRs. Results demonstrate the large impacts of this calibration on the TPW climate trend.

scholarly journals Exploiting Inter- and Intra-Base Crossing with Multi-Mappings: Application to Environmental Data

2018 ◽  
Vol 2 (3) ◽  
pp. 25 ◽  
Author(s):  
Hicham Hajj-Hassan ◽  
Anne Laurent ◽  
Arnaud Martin
Keyword(s):  
Spatial Location ◽  
Point Of View ◽  
Environmental Data ◽  
Sensor Data ◽  
Global Ocean ◽  
Global Changes ◽  
Geospatial Information ◽  
Multiple Points ◽  
Points Of View ◽  
Single Data

Environmental data are currently gaining more and more interest as they are required to understand global changes. In this context, sensor data are collected and stored in dedicated databases. Frameworks have been developed for this purpose and rely on standards, as for instance the Sensor Observation Service (SOS) provided by the Open GeoSpatial Consortium (OGC), where all measurements are bound to a so-called Feature of Interest (FoI). These databases are used to validate and test scientific hypotheses often formulated as correlations and causality between variables, as for instance the study of the correlations between environmental factors and chlorophyll levels in the global ocean. However, the hypotheses of the correlations to be tested are often difficult to formulate as the number of variables that the user can navigate through can be huge. Moreover, it is often the case that the data are stored in such a manner that they prevent scientists from crossing them in order to retrieve relevant correlations. Indeed, the FoI can be a spatial location (e.g., city), but can also be any other object (e.g., animal species). The same data can thus be represented in several manners, depending on the point of view. The FoI varies from one representation to the other one, while the data remain unchanged. In this article, we propose a novel methodology including a crucial step to define multiple mappings from the data sources to these models that can then be crossed, thus offering multiple possibilities that could be hidden from the end-user if using the initial and single data model. These possibilities are provided through a catalog embedding the multiple points of view and allowing the user to navigate through these points of view through innovative OLAP-like operations. It should be noted that the main contribution of this work lies in the use of multiple points of view, as many other works have been proposed for manipulating, aggregating visualizing and navigating through geospatial information. Our proposal has been tested on data from an existing environmental observatory from Lebanon. It allows scientists to realize how biased the representations of their data are and how crucial it is to consider multiple points of view to study the links between the phenomena.

scholarly journals Comparison of the GPCP and CMAP Merged Gauge–Satellite Monthly Precipitation Products for the Period 1979–2001

2004 ◽  
Vol 5 (6) ◽  
pp. 1207-1222 ◽  
Author(s):  
Xungang Yin ◽  
Arnold Gruber ◽  
Phil Arkin
Keyword(s):  
Spatial Correlation ◽  
Input Data ◽  
Sampling Error ◽  
Global Precipitation Climatology Project ◽  
Monthly Precipitation ◽  
Tropical Ocean ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Microwave Imager ◽  
Global Precipitation

Abstract The two monthly precipitation products of the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) are compared on a 23-yr period, January 1979–December 2001. For the long-term mean, major precipitation patterns are clearly demonstrated by both products, but there are differences in the pattern magnitudes. In the tropical ocean the CMAP is higher than the GPCP, but this is reversed in the high-latitude ocean. The GPCP–CMAP spatial correlation is generally higher over land than over the ocean. The correlation between the global mean oceanic GPCP and CMAP is significantly low. It is very likely because the input data of the two products have much less in common over the ocean; in particular, the use of atoll data by the CMAP is disputable. The decreasing trend in the CMAP oceanic precipitation is found to be an artifact of input data change and atoll sampling error. In general, overocean precipitation represented by the GPCP is more reasonable; over land the two products are close, but different merging algorithms between the GPCP and the CMAP can sometimes produce substantial discrepancy in sensitive areas such as equatorial West Africa. EOF analysis shows that the GPCP and the CMAP are similar in 6 out of the first 10 modes, and the first 2 leading modes (ENSO patterns) of the GPCP are nearly identical to their counterparts of the CMAP. Input data changes [e.g., January 1986 for Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI), July 1987 for Special Sensor Microwave Imager (SSM/I), May 1994 for Microwave Sounding Unit (MSU), and January 1996 for atolls] have implications in the behavior of the two datasets. Several abrupt changes identified in the statistics of the two datasets including the changes in overocean precipitation, spatial correlation time series, and some of the EOF principal components, can be related to one or more input data changes.

scholarly journals Consistency of the current global ocean observing systems from an Argo perspective

Ocean Science ◽  
2014 ◽  
Vol 10 (3) ◽  
pp. 547-557 ◽  
Author(s):  
K. von Schuckmann ◽  
J.-B. Sallée ◽  
D. Chambers ◽  
P.-Y. Le Traon ◽  
C. Cabanes ◽  
...  
Keyword(s):  
Sea Level ◽  
Heat Content ◽  
Deep Ocean ◽  
Global Ocean ◽  
Regional Variability ◽  
Tropical Ocean ◽  
Steric Sea Level ◽  
Ocean Observing ◽  
Ocean Observing Systems ◽  
Observing Systems

Abstract. Variations in the world's ocean heat storage and its associated volume changes are a key factor to gauge global warming and to assess the earth's energy and sea level budget. Estimating global ocean heat content (GOHC) and global steric sea level (GSSL) with temperature/salinity data from the Argo network reveals a positive change of 0.5 ± 0.1 W m−2 (applied to the surface area of the ocean) and 0.5 ± 0.1 mm year−1 during the years 2005 to 2012, averaged between 60° S and 60° N and the 10–1500 m depth layer. In this study, we present an intercomparison of three global ocean observing systems: the Argo network, satellite gravimetry from GRACE and satellite altimetry. Their consistency is investigated from an Argo perspective at global and regional scales during the period 2005–2010. Although we can close the recent global ocean sea level budget within uncertainties, sampling inconsistencies need to be corrected for an accurate global budget due to systematic biases in GOHC and GSSL in the Tropical Ocean. Our findings show that the area around the Tropical Asian Archipelago (TAA) is important to closing the global sea level budget on interannual to decadal timescales, pointing out that the steric estimate from Argo is biased low, as the current mapping methods are insufficient to recover the steric signal in the TAA region. Both the large regional variability and the uncertainties in the current observing system prevent us from extracting indirect information regarding deep-ocean changes. This emphasizes the importance of continuing sustained effort in measuring the deep ocean from ship platforms and by beginning a much needed automated deep-Argo network.

scholarly journals A multi-sensor data-driven methodology for all-sky passive microwave inundation retrieval

2017 ◽  
Vol 21 (6) ◽  
pp. 2685-2700 ◽  
Author(s):  
Zeinab Takbiri ◽  
Ardeshir M. Ebtehaj ◽  
Efi Foufoula-Georgiou
Keyword(s):  
Rank Correlation ◽  
Nearest Neighbor Search ◽  
Water Levels ◽  
Passive Microwave ◽  
Sensor Data ◽  
Convective Precipitation ◽  
Retrieval Algorithm ◽  
Inversion Method ◽  
Diurnal Variability ◽  
Microwave Imager

Abstract. We present a multi-sensor Bayesian passive microwave retrieval algorithm for flood inundation mapping at high spatial and temporal resolutions. The algorithm takes advantage of observations from multiple sensors in optical, short-infrared, and microwave bands, thereby allowing for detection and mapping of the sub-pixel fraction of inundated areas under almost all-sky conditions. The method relies on a nearest-neighbor search and a modern sparsity-promoting inversion method that make use of an a priori dataset in the form of two joint dictionaries. These dictionaries contain almost overlapping observations by the Special Sensor Microwave Imager and Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F17 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites. Evaluation of the retrieval algorithm over the Mekong Delta shows that it is capable of capturing to a good degree the inundation diurnal variability due to localized convective precipitation. At longer timescales, the results demonstrate consistency with the ground-based water level observations, denoting that the method is properly capturing inundation seasonal patterns in response to regional monsoonal rain. The calculated Euclidean distance, rank-correlation, and also copula quantile analysis demonstrate a good agreement between the outputs of the algorithm and the observed water levels at monthly and daily timescales. The current inundation products are at a resolution of 12.5 km and taken twice per day, but a higher resolution (order of 5 km and every 3 h) can be achieved using the same algorithm with the dictionary populated by the Global Precipitation Mission (GPM) Microwave Imager (GMI) products.

scholarly journals Fundamental Climate Data Records of Microwave Brightness Temperatures

2018 ◽  
Vol 10 (8) ◽  
pp. 1306 ◽  
Author(s):  
Wesley Berg ◽  
Rachael Kroodsma ◽  
Christian Kummerow ◽  
Darren McKague
Keyword(s):  
Land Surface ◽  
Surface Wind ◽  
Precipitable Water ◽  
Original Data ◽  
Climate Data ◽  
Sea Ice Extent ◽  
Brightness Temperatures ◽  
Precipitation Total ◽  
Data Record ◽  
Microwave Imager

An intercalibrated Fundamental Climate Data Record (FCDR) of brightness temperatures (Tb) has been developed using data from a total of 14 research and operational conical-scanning microwave imagers. This dataset provides a consistent 30+ year data record of global observations that is well suited for retrieving estimates of precipitation, total precipitable water, cloud liquid water, ocean surface wind speed, sea ice extent and concentration, snow cover, soil moisture, and land surface emissivity. An initial FCDR was developed for a series of ten Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments on board the Defense Meteorological Satellite Program spacecraft. An updated version of this dataset, including additional NASA and Japanese sensors, has been developed as part of the Global Precipitation Measurement (GPM) mission. The FCDR development efforts involved quality control of the original data, geolocation corrections, calibration corrections to account for cross-track and time-dependent calibration errors, and intercalibration to ensure consistency with the calibration reference. Both the initial SSMI(S) and subsequent GPM Level 1C FCDR datasets are documented, updated in near real-time, and publicly distributed.

Implications of a regional-scale process (the Lakshadweep low) on the mesozooplankton community structure of the Arabian Sea

2019 ◽  
Vol 70 (3) ◽  
pp. 345 ◽  
Author(s):  
K. K. Karati ◽  
G. Vineetha ◽  
T. V. Raveendran ◽  
P. K. Dineshkumar ◽  
K. R. Muraleedharan ◽  
...  
Keyword(s):  
Physical Process ◽  
Arabian Sea ◽  
Mixed Layer Depth ◽  
Regional Scale ◽  
Zooplankton Community ◽  
Ocean Basin ◽  
Zooplankton Biomass ◽  
Global Ocean ◽  
Mesozooplankton Community ◽  
Tropical Ocean

The Arabian Sea, a major tropical ocean basin in the northern Indian Ocean, is one of the most productive regions in the global ocean. Although the classical Arabian Sea ‘paradox’ describes the geographical and seasonal invariability in zooplankton biomass in this region, the effect of the Lakshadweep low (LL), a regional-scale physical process, on the zooplankton community has not yet been evaluated. The LL, characterised by low sea surface height and originating around the vicinity of the Lakshadweep islands during the mid-summer monsoon, is unique to the Arabian Sea. The present study investigated the effect of the LL on the zooplankton community. The LL clearly had a positive effect, with enhanced biomass and abundance in the mixed-layer depth of the LL region. Copepods and chaetognaths formed the dominant taxa, exhibiting strong affinity towards the physical process. Of the 67 copepod species observed, small copepods belonging to the families Paracalanidae, Clausocalanidae, Calanidae, Oncaeidae and Corycaeidae dominated the LL region. Phytoplankton biomass (chlorophyll-a) was the primary determinant influencing the higher preponderance of the copepod community in this region.

scholarly journals The AMSU-Based Hydrological Bundle Climate Data Record—Description and Comparison with Other Data Sets

2018 ◽  
Vol 10 (10) ◽  
pp. 1640 ◽  
Author(s):  
Ralph Ferraro ◽  
Brian Nelson ◽  
Tom Smith ◽  
Olivier Prat
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Precipitable Water ◽  
Climate Data ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Data Record ◽  
Microwave Imager ◽  
Global Precipitation ◽  
Climate Data Record

Passive microwave measurements have been available on satellites back to the 1970s, first flown on research satellites developed by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications (e.g., the Special Sensor Microwave/Imager—SSM/I; the Advanced Microwave Sounding Unit—AMSU) and climate applications (e.g., the Advanced Microwave Scanning Radiometer—AMSR; the Tropical Rainfall Measurement Mission Microwave Imager—TMI; the Global Precipitation Mission Microwave Imager—GMI). Here, the focus is on measurements from the AMSU-A, AMSU-B, and Microwave Humidity Sounder (MHS). These sensors have been in operation since 1998, with the launch of NOAA-15, and are also on board NOAA-16, -17, -18, -19, and the MetOp-A and -B satellites. A data set called the “Hydrological Bundle” is a climate data record (CDR) that utilizes brightness temperatures from fundamental CDRs (FCDRs) to generate thematic CDRs (TCDRs). The TCDRs include total precipitable water (TPW), cloud liquid water (CLW), sea-ice concentration (SIC), land surface temperature (LST), land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE). The TCDRs are shown to be in general good agreement with similar products from other sources, such as the Global Precipitation Climatology Project (GPCP) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). Due to the careful intercalibration of the FCDRs, little bias is found among the different TCDRs produced from individual NOAA and MetOp satellites, except for normal diurnal cycle differences.

scholarly journals Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3456
Author(s):  
Robin Kraft ◽  
Ferdinand Birk ◽  
Manfred Reichert ◽  
Aniruddha Deshpande ◽  
Winfried Schlee ◽  
...  
Keyword(s):  
Architectural Design ◽  
Environmental Data ◽  
Common Interest ◽  
Sensor Data ◽  
Smart Sensors ◽  
Use Case ◽  
Sound Levels ◽  
Efficient Processing ◽  
Interactive Map ◽  
Technical Solutions

Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.

scholarly journals Retrieval of water vapor using SSM/I and its relation with the onset of monsoon

2004 ◽  
Vol 22 (8) ◽  
pp. 3079-3083 ◽  
Author(s):  
R. P. Singh ◽  
S. Dey ◽  
A. K. Sahoo ◽  
M. Kafatos
Keyword(s):  
Water Vapor ◽  
Interannual Variability ◽  
Seasonal Variations ◽  
Indian Monsoon ◽  
Precipitable Water ◽  
Climatic Conditions ◽  
Satellite Monitoring ◽  
Onset Date ◽  
Microwave Imager ◽  
Onset Of Monsoon

Abstract. The seasonal variations and interannual variability of total precipitable water (TPW) deduced from the Special Sensor Microwave Imager (SSM/I) satellite over oceanic regions of the Indian sub-continent during the years between 1988 to 1998 show characteristic behavior. The weekly patterns of TPW are found to be closely related to the dynamics of the climatic conditions and the onset date of monsoon. The present results show that the satellite monitoring of TPW may prove as a good and reliable indicator in forecasting Indian monsoon.

scholarly journals IMERG V06: Changes to the Morphing Algorithm

2019 ◽  
Vol 36 (12) ◽  
pp. 2471-2482 ◽  
Author(s):  
Jackson Tan ◽  
George J. Huffman ◽  
David T. Bolvin ◽  
Eric J. Nelkin
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Precipitable Water ◽  
Passive Microwave ◽  
Precipitation Field ◽  
Motion Vectors ◽  
Surface Precipitation ◽  
General Improvement ◽  
Lagrangian Interpolation ◽  
Cloud Tops

AbstractAs the U.S. Science Team’s globally gridded precipitation product from the NASA–JAXA Global Precipitation Measurement (GPM) mission, the Integrated Multi-Satellite Retrievals for GPM (IMERG) estimates the surface precipitation rates at 0.1° every half hour using spaceborne sensors for various scientific and societal applications. One key component of IMERG is the morphing algorithm, which uses motion vectors to perform quasi-Lagrangian interpolation to fill in gaps in the passive microwave precipitation field using motion vectors. Up to IMERG V05, the motion vectors were derived from the large-scale motions of infrared observations of cloud tops. This study details the changes introduced in IMERG V06 to derive motion vectors from large-scale motions of selected atmospheric variables in numerical models, which allow IMERG estimates to be extended from the 60°N–60°S latitude band to the entire globe. Evaluation against both instantaneous passive microwave retrievals and ground measurements demonstrates the general improvement in the precipitation field of the new approach. Most of the model variables tested exhibited similar performance, but total precipitable water vapor was chosen as the source of the motion vectors for IMERG V06 due to its competitive performance and global completeness. Continuing assessments will provide further insights into possible refinements of this revised morphing scheme in future versions of IMERG.

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