Long-Term Consistent Recalibration of VIRR Solar Reflectance Data Record for Fengyun Polar-Orbiting Satellites

2021 ◽  
Vol 35 (6) ◽  
pp. 926-942
Author(s):  
Ling Sun ◽  
Hong Qiu ◽  
Ronghua Wu ◽  
Jing Wang ◽  
Liyang Zhang ◽  
...  
Keyword(s):  
Solar Reflectance ◽  
Data Record ◽  
Reflectance Data

Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record

2010 ◽  
Vol 31 (24) ◽  
pp. 6493-6517 ◽  
Author(s):  
Andrew K. Heidinger ◽  
William C. Straka ◽  
Christine C. Molling ◽  
Jerry T. Sullivan ◽  
Xiangqian Wu
Keyword(s):  
Solar Reflectance ◽  
Data Record ◽  
Reflectance Data

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.

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 Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework

2008 ◽  
Vol 112 (6) ◽  
pp. 2938-2949 ◽  
Author(s):  
Randall J. Donohue ◽  
Michael L. Roderick ◽  
Tim R. McVicar
Keyword(s):  
Reflectance Data

scholarly journals SCIAMACHY Absorbing Aerosol Index – calibration issues and global results from 2002–2004

2005 ◽  
Vol 5 (3) ◽  
pp. 3367-3389 ◽  
Author(s):  
M. de Graaf ◽  
P. Stammes
Keyword(s):  
Good Representation ◽  
Correction Factors ◽  
Lookup Tables ◽  
Scanning Imaging ◽  
Aerosol Index ◽  
Reflectance Data ◽  
Absorption Spectrometer

Abstract. The validity of the Absorbing Aerosol Index (AAI) product from the SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) is discussed. The operational SCIAMACHY AAI product suffers from calibration errors in the reflectance as measured by SCIAMACHY and design errors. Therefore, the AAI product was recalculated, compensating for the errors, with reflectance data from the start of measurements of SCIAMACHY until December 2004. Appropriate correction factors were determined for the UV to correct for the radiometric error in the SCIAMACHY reflectances. The algorithm was provided with LookUp Tables in which a good representation of polarisation effects was incorporated, as opposed to the LookUp Tables of the operational product, in which polarisation effects were not accounted for. The results are presented, their validity discussed, and compared to the operational product. The AAI is very sensitive to calibration errors and can be used to monitor calibration errors and changes. From 2004 onwards, the new SCIAMACHY AAI is suitable to add to the continuation of the long-term AAI record. Recommendations are given for improvement of the operational AAI product.

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>

Twitter Based Capital Market Analysis Using Cloud Statistics

2019 ◽  
Vol 11 (2) ◽  
pp. 54-60 ◽  
Author(s):  
Sangeeta Gupta ◽  
Rajanikanth Aluvalu
Keyword(s):  
Capital Market ◽  
Missing Values ◽  
Market Analysis ◽  
Social Networking Site ◽  
Unstructured Data ◽  
Modern World ◽  
Cloud Environment ◽  
Statistical Measure ◽  
Data Record

People in the modern world are attracted towards smart working and earning environments rather than having a long-term perception. The goal of this work is to address the challenge of providing better inputs to the customers interested to investing in the share market to earn better returns on investments. The Twitter social networking site is chosen to develop the proposed environment as a majority of the customers tweet about their opinions. A huge set of data across various companies that take inputs from Twitter are processed and stored in the cloud environment for efficient analysis and assessment. A statistical measure is used to signal the worth of investing in a particular stock based on the outcomes obtained. Also, rather than ignoring the missing values and unstructured data, the proposed work analyzes every single entity to enable the customers to take worthy decisions. Tweets in the range of 1 to 100,000 are taken to perform analysis and it is observed from the results that for a maximum of 100,000 tweets, the number of missing is identified as 2,524 and the statistical measure to fill in the missing values is calculated based on the particular missing data record, the count of all data records, and the total number of records. If the outcome of the measure is obtained as a negative, then proceeding with an investment is not recommended. The findings of this work will help the share market investors to earn better profits.

scholarly journals Long-Term Variations in the Pixel-to-Pixel Variability of NOAA AVHRR SST Fields from 1982 to 2015

2019 ◽  
Vol 11 (7) ◽  
pp. 844 ◽  
Author(s):  
Fan Wu ◽  
Peter Cornillon ◽  
Lei Guan ◽  
Katherine Kilpatrick
Keyword(s):  
Quality Data ◽  
Retrieval Algorithm ◽  
Ocean Fronts ◽  
Noaa Avhrr ◽  
Data Record ◽  
Along Track ◽  
Level 2 ◽  
Very High ◽  
Significant Effort

Sea surface temperature (SST) fields obtained from the series of space-borne five-channel Advanced Very High Resolution Radiometers (AVHRRs) provide the longest continuous time series of global SST available to date (1981–present). As a result, these data have been used for many studies and significant effort has been devoted to their careful calibration in an effort to provide a climate quality data record. However, little attention has been given to the local precision of the SST retrievals obtained from these instruments, which we refer to as the pixel-to-pixel (p2p) variability, a characteristic important in the ability to resolve structures such as ocean fronts characterized by small gradients in the SST field. In this study, the p2p variability is estimated for Level-2 SST fields obtained with the Pathfinder retrieval algorithm for AVHRRs on NOAA-07, 9, 11, 12 and 14-19. These estimates are stratified by year, season, day/night and along-scan/along-track. The overall variability ranges from 0.10 K to 0.21 K. For each satellite, the along-scan variability is between 10 and 20% smaller than the along-track variability (except for NOAA-16 nighttime for which it is approximately 30% smaller) and the summer and fall σ s are between 10 and 15% smaller than the winter and spring σ s. The differences between along-track and along-scan are attributed to the way in which the instrument has been calibrated. The seasonal differences result from the T 4 − T 5 term in the Pathfinder retrieval algorithm. This term is shown to be a major contributor to the p2p variability and it is shown that its impact could be substantially reduced without a deleterious effect on the overall p2p σ of the resulting products by spatially averaging it as part of the retrieval process. The AVHRR/3s (NOAA-15 through 19) were found to be relatively stable with trends in the p2p variability of at most 0.015 K/decade.

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