Identification and correction of systematic error in NOAA AVHRR long-term satellite data record

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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New Data Record Extends History of Global Air Pollution

Eos ◽

10.1029/2017eo079895 ◽

2017 ◽

Author(s):

Sarah Witman

Keyword(s):

Air Pollution ◽

Satellite Data ◽

The Past ◽

Data Record ◽

History Of

Researchers extend long-term aerosol records to the past 40 years by combining two existing algorithms to process satellite data over both land and sea.

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Alpine cloud climatology using long-term NOAA-AVHRR satellite data

Theoretical and Applied Climatology ◽

10.1007/s007040170044 ◽

2001 ◽

Vol 68 (3-4) ◽

pp. 175-195 ◽

Cited By ~ 6

Author(s):

M. Kästner ◽

K. T. Kriebel

Keyword(s):

Satellite Data ◽

Noaa Avhrr

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SATELLITE DATA FOR INVESTIGATION OF RECENT STATE AND PROCESSES IN THE SIVASH BAY

Proceedings of International Conference "Managinag risks to coastal regions and communities in a changinag world" (EMECS'11 - SeaCoasts XXVI) ◽

10.21610/conferencearticle_58b43159b0693 ◽

2017 ◽

Author(s):

Ekaterina Shchurova ◽

Rimma Stanichnaya ◽

Sergey Stanichny ◽

...

Keyword(s):

Satellite Data ◽

Water Exchange ◽

High Salinity ◽

Meteorological Data ◽

Sea Surface ◽

Azov Sea ◽

Seasonal Wind ◽

Ice Regime ◽

Very High

Sivash bay is the shallow-water lagoon of the Azov Sea. Restricted water exchange and high evaporation form Sivash as the basin with very high salinity. This factor leads to different from the Azov Sea thermal and ice regimes of Sivash. Maine aim of the study presented to investigate recent state and changes of the characteristics and processes in the basin using satellite data. Landsat scanners TM, ETM+, OLI, TIRS together with MODIS and AVHRR were used. Additionally NOMADS NOAA and MERRA meteorological data were analyzed. The next topics are discussed in the work: 1. Changes of the sea surface temperature, ice regime and relation with salinity. 2. Coastal line transformation – long term and seasonal, wind impact. 3. Manifestation of the Azov waters intrusions through the Arabat spit, preferable wind conditions.

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

Scientific Data ◽

10.1038/s41597-021-00940-9 ◽

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.

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

Remote Sensing ◽

10.3390/rs13091701 ◽

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.

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