scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

2015 ◽  
Vol 96 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Hamed Ashouri ◽  
Kuo-Lin Hsu ◽  
Soroosh Sorooshian ◽  
Dan K. Braithwaite ◽  
Kenneth R. Knapp ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Radar Data ◽  
Climate Data ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record ◽  
Climate Studies ◽  
Rainfall Estimates ◽  
Good Agreement

Abstract A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the probability density function (PDF) of PERSIANN-CDR over the contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydroclimate studies in regional and global scales.

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 6 (1) ◽  
pp. 95-117
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 × 25 km grid cells in both the Southern and Northern Hemisphere Polar Regions from 9 July 1987 to 31 December 2007 with an update through 2011 underway. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Oceanic and Atmospheric Administration (NOAA)'s National Climatic Data Center (NCDC) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The dataset along with detailed data processing steps and error source information can be found at: doi:10.7265/N5B56GN3.

Development of a microwave-based precipitation climate data record for the Copernicus Climate Change Service

2020 ◽  
Author(s):  
Giulia Panegrossi ◽  
Paolo Sanò ◽  
Leonardo Bagaglini ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Satellite Observations ◽  
Passive Microwave ◽  
Monthly Precipitation ◽  
Climate Data ◽  
Data Record ◽  
Level 3 ◽  
Climate Data Record ◽  
Level 2

<p>Within the Copernicus Climate Change Service (C3S), the Climate Data Store (CDS) built by ECMWF will provide open and free access to global and regional products of Essential Climate Variables (ECV) based on satellite observations spanning several decades, amongst other things. Given its significance in the Earth system and particularly for human life, the ECV precipitation will be of major interest for users of the CDS.</p><p>C3S strives to include as many established, high-quality data sets as possible in the CDS. However, it also intends to offer new products dedicated for first-hand publication in the CDS. One of these products is a climate data record based on merging satellite observations of daily and monthly precipitation by both passive microwave (MW) sounders (AMSU-B/MHS) and imagers (SSMI/SSMIS) on a 1°x1° spatial grid in order to improve spatiotemporal satellite coverage of the globe.</p><p>The MW sounder observations will be obtained using, as input data, the FIDUCEO Fundamental Climate data Record (FCDR) for AMSU-B/MHS in a new global algorithm developed specifically for the project based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR; Sanò et al., 2015), adapted for climate applications (PNPR-CLIM). The algorithm consists of two Artificial Neural Network-based modules, one for precipitation detection, and one for precipitation rate estimate, trained on a global observational database built from Global Precipitation Measurement-Core Observatory (GPM-CO) measurements. The MW imager observations by SSM/I and SSMIS will be adopted from the Hamburg Ocean Atmosphere Fluxes and Parameters from Satellite data (HOAPS; Andersson et al., 2017), based on the CM SAF SSM/I and SSMIS FCDR (Fennig et al., 2017). The Level 2 precipitation rate estimates from MW sounders and imagers are combined through a newly developed merging module to obtain Level 3 daily and monthly precipitation and generate the 18-year precipitation CDR (2000-2017).</p><p>Here, we present the status of the Level 2 product’s development. We carry out a Level-2 comparison and present first results of the merged Level-3 precipitation fields. Based on this, we assess the product’s expected plausibility, coverage, and the added value of merging the MW sounder and imager observations.</p><p><strong>References</strong></p><p>Anderssonet al., 2017, DOI:10.5676/EUM_SAF_CM/HOAPS/V002</p><p>Fennig, et al., 2017, DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003</p><p>Sanò, P., et al., 2015, DOI: 10.5194/amt-8-837-2015</p>

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 5 (2) ◽  
pp. 311-318 ◽  
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N55M63M1.

Towards a continuous NASA cloud climate data record from MODIS to VIIRS

2021 ◽  
Author(s):  
Kerry Meyer ◽  
Steven Platnick ◽  
Robert Holz ◽  
Steven Ackerman ◽  
Andrew Heidinger ◽  
...  
Keyword(s):  
Observation Time ◽  
Radiometric Calibration ◽  
Cloud Detection ◽  
Climate Data ◽  
Cloud Data ◽  
Microphysical Properties ◽  
Data Record ◽  
Climate Data Record ◽  
Relative Radiometric Calibration

<p>The Suomi NPP and JPSS series VIIRS imagers provide an opportunity to extend the NASA EOS Terra (20+ year) and Aqua (18+ year) MODIS cloud climate data record into the new generation NOAA operational weather satellite era. However, while building a consistent, long-term cloud data record has proven challenging for the two MODIS sensors alone, the transition to VIIRS presents additional challenges due to its lack of key water vapor and CO<sub>2</sub> absorbing channels available on MODIS that are used for high cloud detection and cloud-top property retrievals, and a mismatch in the spectral location of the 2.2µm shortwave infrared channels on MODIS and VIIRS that has important implications on inter-sensor consistency of cloud optical/microphysical property retrievals and cloud thermodynamic phase. Moreover, sampling differences between MODIS and VIIRS, including spatial resolution and local observation time, and inter-sensor relative radiometric calibration pose additional challenges. To create a continuous, long-term cloud climate data record that merges the observational records of MODIS and VIIRS while mitigating the impacts of these sensor differences, a common algorithm approach was pursued that utilizes a subset of spectral channels available on each imager. The resulting NASA CLDMSK (cloud mask) and CLDPROP (cloud-top and optical/microphysical properties) products were publicly released for Aqua MODIS and SNPP VIIRS in early 2020, with NOAA-20 (JPSS-1) VIIRS following in early 2021. Here, we present an overview of the MODIS-VIIRS CLDMSK and CLDPROP common algorithm approach, discuss efforts to monitor and address relative radiometric calibration differences, and highlight early analysis of inter-sensor cloud product dataset continuity.</p>

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 An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders

2019 ◽  
Vol 11 (5) ◽  
pp. 548 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan Buehler ◽  
Marc Prange ◽  
Theresa Lang ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Uncertainty Propagation ◽  
Uncertainty Budget ◽  
Intermediate Step ◽  
Climate Data ◽  
Data Record ◽  
Upper Tropospheric Humidity ◽  
Uncertainty Information ◽  
Climate Data Record

To date, there is no long-term, stable, and uncertainty-quantified dataset of upper tropospheric humidity (UTH) that can be used for climate research. As intermediate step towards the overall goal of constructing such a climate data record (CDR) of UTH, we produced a new fundamental climate data record (FCDR) on the level of brightness temperature for microwave humidity sounders that will serve as basis for the CDR of UTH. Based on metrological principles, we constructed and implemented the measurement equation and the uncertainty propagation in the processing chain for the microwave humidity sounders. We reprocessed the level 1b data to obtain newly calibrated uncertainty quantified level 1c data in brightness temperature. Three aspects set apart this FCDR from previous attempts: (1) the data come in a ready-to-use NetCDF format; (2) the dataset provides extensive uncertainty information taking into account the different correlation behaviour of the underlying errors; and (3) inter-satellite biases have been understood and reduced by an improved calibration. Providing a detailed uncertainty budget on these data, this new FCDR provides valuable information for a climate scientist and also for the construction of the CDR.

scholarly journals Stratospheric Temperature Climate Data Record from Merged SSU and AMSU-A Observations

2016 ◽  
Vol 33 (9) ◽  
pp. 1967-1984 ◽  
Author(s):  
Cheng-Zhi Zou ◽  
Haifeng Qian
Keyword(s):  
Piecewise Linear ◽  
Vital Role ◽  
Climate Data ◽  
Climate Trends ◽  
Weighting Functions ◽  
Stratospheric Temperature ◽  
Data Record ◽  
Temperature Climate ◽  
Climate Data Record

AbstractObservations from the Stratospheric Sounding Unit (SSU) on board historical NOAA polar-orbiting satellites have played a vital role in investigations of long-term trends and variability in the middle- and upper-stratospheric temperatures during 1979–2006. The successor to SSU is the Advanced Microwave Sounding Unit-A (AMSU-A) starting from 1998 until the present. Unfortunately, the two observations came from different sets of atmospheric layers, and the SSU weighting functions varied with time and location, posing a challenge to merge them with sufficient accuracy for development of an extended SSU climate data record. This study proposes a variational approach for the merging problem, matching in both temperatures and weighting functions. The approach yields zero means with a small standard deviation and a negligible drift over time in the temperature differences between SSU and its extension to AMSU-A. These features made the approach appealing for reliable detection of long-term climate trends. The approach also matches weighting functions with high accuracy for SSU channels 1 and 2 and reasonable accuracy for channel 3. The total decreases in global mean temperatures found from the merged dataset were from 1.8 K in the middle stratosphere to 2.4 K in the upper stratosphere during 1979–2015. These temperature drops were associated with two segments of piecewise linear cooling trends, with those during the first period (1979–97) being much larger than those of the second period (1998–2015). These differences in temperature trends corresponded well to changes of the atmospheric ozone amount from depletion to recovery during the respective time periods, showing the influence of human decisions on climate change.

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