scholarly journals HIRS Outgoing Longwave Radiation—Daily Climate Data Record: Application toward Identifying Tropical Subseasonal Variability

2018 ◽  
Vol 10 (9) ◽  
pp. 1325 ◽  
Author(s):  
Carl Schreck ◽  
Hai-Tien Lee ◽  
Kenneth Knapp
Keyword(s):  
High Resolution ◽  
Outgoing Longwave Radiation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Longwave Radiation ◽  
Climate Data ◽  
Convective Systems ◽  
Subseasonal Variability ◽  
Data Record ◽  
Climate Data Record

This study describes the development of a new globally gridded climate data record (CDR) for daily outgoing longwave radiation (OLR) using the High-Resolution Infrared Radiation Sounder (HIRS) sensor. The new product, hereafter referred to as HIRS OLR, has several differences and advantages over the widely-used daily OLR dataset derived from the Advanced Very High-Resolution Radiometer (AVHRR) sensor on the same NOAA Polar Operational Environmental Satellites (POES), hereafter AVHRR OLR. As a CDR, HIRS OLR has been intersatellite-calibrated to provide the most homogeneous record possible. AVHRR OLR only used the daytime and nighttime overpasses from a single satellite at a time, which creates some challenges for resolving the large diurnal cycle of OLR. HIRS OLR leverages all available overpasses and then calibrates geostationary estimates of OLR to represent that cycle more faithfully. HIRS also has more spectral channels, including those for measuring water vapor, which provides a more accurate measure of OLR. This difference is particularly relevant for large-scale convective systems such as the El Niño–Southern Oscillation and the Madden–Julian Oscillation, whereby the HIRS OLR can better identify the subtropical variability between the tropical convection and the extratropical teleconnections.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

scholarly journals Development of the HIRS Outgoing Longwave Radiation Climate Dataset

2007 ◽  
Vol 24 (12) ◽  
pp. 2029-2047 ◽  
Author(s):  
Hai-Tien Lee ◽  
Arnold Gruber ◽  
Robert G. Ellingson ◽  
Istvan Laszlo
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Outgoing Longwave Radiation ◽  
Longwave Radiation ◽  
Radiation Budget ◽  
Validation Data ◽  
The Earth ◽  
Data Record ◽  
Earth Radiation Budget ◽  
Earth Radiation

Abstract The Advanced Very High Resolution Radiometer (AVHRR) outgoing longwave radiation (OLR) product, which NOAA has been operationally generating since 1979, is a very long data record that has been used in many applications, yet past studies have shown its limitations and several algorithm-related deficiencies. Ellingson et al. have developed the multispectral algorithm that largely improved the accuracy of the narrowband-estimated OLR as well as eliminated the problems in AVHRR. NOAA has been generating High Resolution Infrared Radiation Sounder (HIRS) OLR operationally since September 1998. In recognition of the need for a continuous and long OLR data record that would be consistent with the earth radiation budget broadband measurements in the National Polar-orbiting Operational Environmental Satellite System (NPOESS) era, and to provide a climate data record for global change studies, a vigorous reprocessing of the HIRS radiance for OLR derivation is necessary. This paper describes the development of the new HIRS OLR climate dataset. The HIRS level 1b data from the entire Television and Infrared Observation Satellite N-series (TIROS-N) satellites have been assembled. A new radiance calibration procedure was applied to obtain more accurate and consistent HIRS radiance measurements. The regression coefficients of the HIRS OLR algorithm for all satellites were rederived from calculations using an improved radiative transfer model. Intersatellite calibrations were performed to remove possible discontinuity in the HIRS OLR product from different satellites. A set of global monthly diurnal models was constructed consistent with the HIRS OLR retrievals to reduce the temporal sampling errors and to alleviate an orbital-drift-induced artificial trend. These steps significantly improved the accuracy, continuity, and uniformity of the HIRS monthly mean OLR time series. As a result, the HIRS OLR shows a comparable stability as in the Earth Radiation Budget Satellite (ERBS) nonscanner OLR measurements. HIRS OLR has superb agreement with the broadband observations from Earth Radiation Budget Experiment (ERBE) and Clouds and the Earth’s Radiant Energy System (CERES) in the ENSO-monitoring regions. It shows compatible ENSO-monitoring capability with the AVHRR OLR. Globally, HIRS OLR agrees with CERES with an accuracy to within 2 W m−2 and a precision of about 4 W m−2. The correlation coefficient between HIRS and CERES global monthly mean is 0.997. Regionally, HIRS OLR agrees with CERES to within 3 W m−2 with precisions better than 3 W m−2 in most places. HIRS OLR could be used for constructing climatology for applications that plan to use NPOESS ERBS and previously used AVHRR OLR observations. The HIRS monthly mean OLR data have high accuracy and precision with respect to the broadband observations of ERBE and CERES. It can be used as an independent validation data source. The uniformity and continuity of HIRS OLR time series suggest that it could be used as a reliable transfer reference for the discontinuous broadband measurements from ERBE, CERES, and ERBS.

Short-Term Basin-Scale Streamflow Forecasting Using Large-Scale Coupled Atmospheric–Oceanic Circulation and Local Outgoing Longwave Radiation

2010 ◽  
Vol 11 (2) ◽  
pp. 370-387 ◽  
Author(s):  
Rajib Maity ◽  
S. S. Kashid
Keyword(s):  
Genetic Programming ◽  
Outgoing Longwave Radiation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Longwave Radiation ◽  
Programming Approach ◽  
Complex Relationship ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Basin Scale

Abstract This paper investigates the use of large-scale circulation patterns (El Niño–Southern Oscillation and the equatorial Indian Ocean Oscillation), local outgoing longwave radiation (OLR), and previous streamflow information for short-term (weekly) basin-scale streamflow forecasting. To model the complex relationship between these inputs and basin-scale streamflow, an artificial intelligence approach—genetic programming (GP)—has been employed. Research findings of this study indicate that the use of large-scale atmospheric circulation information and streamflow at previous time steps, along with OLR as a local meteorological input, potentially improves the performance of weekly basin-scale streamflow prediction. The genetic programming approach is found to capture the complex relationship between the weekly streamflow and various inputs. Different input variable combinations were explored to come up with the best one. The observed and predicted streamflows were found to correspond well with each other with a coefficient of determination of 0.653 (correlation coefficient r = 0.808), which may appear attractive for such a complex system.

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 Estimating the Earth’s Outgoing Longwave Radiation Measured from a Moon-Based Platform

2021 ◽  
Vol 13 (11) ◽  
pp. 2201
Author(s):  
Hanlin Ye ◽  
Huadong Guo ◽  
Guang Liu ◽  
Jinsong Ping ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Outgoing Longwave Radiation ◽  
Large Scale ◽  
Single Point ◽  
Longwave Radiation ◽  
Earth Observation ◽  
Artificial Satellites ◽  
Earth Observations ◽  
The Earth ◽  
Observation Geometry ◽  
Global Mean

Moon-based Earth observations have attracted significant attention across many large-scale phenomena. As the only natural satellite of the Earth, and having a stable lunar surface as well as a particular orbit, Moon-based Earth observations allow the Earth to be viewed as a single point. Furthermore, in contrast with artificial satellites, the varied inclination of Moon-based observations can improve angular samplings of specific locations on Earth. However, the potential for estimating the global outgoing longwave radiation (OLR) from the Earth with such a platform has not yet been fully explored. To evaluate the possibility of calculating OLR using specific Earth observation geometry, we constructed a model to estimate Moon-based OLR measurements and investigated the potential of a Moon-based platform to acquire the necessary data to estimate global mean OLR. The primary method of our study is the discretization of the observational scope into various elements and the consequent integration of the OLR of all elements. Our results indicate that a Moon-based platform is suitable for global sampling related to the calculation of global mean OLR. By separating the geometric and anisotropic factors from the measurement calculations, we ensured that measured values include the effects of the Moon-based Earth observation geometry and the anisotropy of the scenes in the observational scope. Although our results indicate that higher measured values can be achieved if the platform is located near the center of the lunar disk, a maximum difference between locations of approximately 9 × 10−4 W m−2 indicates that the effect of location is too small to remarkably improve observation performance of the platform. In conclusion, our analysis demonstrates that a Moon-based platform has the potential to provide continuous, adequate, and long-term data for estimating global mean OLR.

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 CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data

2017 ◽  
Vol 17 (9) ◽  
pp. 5809-5828 ◽  
Author(s):  
Karl-Göran Karlsson ◽  
Kati Anttila ◽  
Jörg Trentmann ◽  
Martin Stengel ◽  
Jan Fokke Meirink ◽  
...  
Keyword(s):  
Surface Albedo ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Climate Data ◽  
Ground Segment ◽  
Surface Radiation Budget ◽  
Avhrr Data ◽  
Data Record ◽  
Development And Validation ◽  
Climate Data Record

Abstract. The second edition of the satellite-derived climate data record CLARA (The CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data – second edition denoted as CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail, as are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing the basic AVHRR level-1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Arctic summer surface albedo and cloud conditions are provided.

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