scholarly journals The Impact of COVID-19 Lockdowns on Satellite-Observed Aerosol Optical Thickness over the Surrounding Coastal Oceanic Areas of Megacities in the Coastal Zone

Geographies ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 381-397
Author(s):  
Kai Wang ◽  
Xuepeng Zhao
Keyword(s):  
Optical Thickness ◽  
Satellite Observation ◽  
Dust Storms ◽  
Aerosol Optical Thickness ◽  
Climate Data ◽  
Aerosol Emission ◽  
The Impact ◽  
Climate Data Record ◽  
Global Oceans

Nearly 40 years of aerosol optical thickness (AOT) climate data record (CDR) derived from NOAA operational satellite Advanced Very High Resolution Radiometer (AVHRR) observation over the global oceans is used to study the AOT changes due to the COVID-19 lockdown over the surrounding coastal oceanic areas of 18 megacities in the coast zone (MCCZ). The AOT difference between the annual mean AOT values of 2020 with COVID-19 lockdown and 2019 without the lockdown along with the 2020 AOT annual anomaly are used to effectively identify the AOT changes that are a result of the lockdown. We found that for most of the 18 MCCZ, the COVID-19 lockdowns implemented to contain the spread of the coronavirus resulted in a decrease between 1% and 30% in AOT due to reduced anthropogenic emissions associated with the lockdowns. However, the AOT long-term trend and other aerosol interannual variations due to favorable or unfavorable meteorological conditions may mask AOT changes due to the lockdown effect in some MCCZ. Different seasonal variations of aerosol amount in 2020 relative to 2019 due to other natural aerosol emission sources not influenced by the lockdown, such as dust storms and natural biomass burning and smoke, may also conceal a limited reduction in the annual mean AOT due to the lockdown in MCCZ with relatively loose lockdown. This study indicates that the use of long-term satellite observation is helpful for studying and monitoring the aerosol changes due to the emission reduction associated with the COVID-19 lockdown in the surrounding coastal oceanic areas of MCCZ, which will benefit the future development of the mitigation strategy for air pollution and emissions in megacities.

scholarly journals Long-Term Aerosol Climate Data Record Derived from Operational AVHRR Satellite Observations

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
P. K. Chan ◽  
X.-P. Zhao ◽  
A. K. Heidinger
Keyword(s):  
Climate Forcing ◽  
Global Ocean ◽  
Climatic Data ◽  
Climate Data ◽  
Clear Sky ◽  
National Climatic Data Center ◽  
Data Record ◽  
Climate Data Record ◽  
Global Oceans

Aerosol optical thickness (AOT) was retrieved using the Advanced Very High Resolution Radiometer (AVHRR) PATMOS-x Level-2b gridded radiances and the two-channel algorithm of the National Climatic Data Center (NCDC). The primary retrieval product is AOT at 0.63 μm channel. AOT is also retrieved at 0.83 μm or 1.61 μm channel for consistent check. The retrieval was made during day time, under clear sky and snow-free conditions, and over the global oceans. The spatial resolution is 0.1×0.1 degree grid and the temporal resolution is both daily and monthly. The resultant AVHRR AOT climate data record (CDR) spans from August 1981 to December 2009 and provides the longest aerosol CDR currently available from operational satellites. This dataset is useful in studying aerosol climate forcing, monitoring long-term aerosol trends, and evaluating global air pollution and aerosol transport models over the global ocean.

scholarly journals Satellite Observed Aerosol Optical Thickness and Trend around Megacities in the Coastal Zone

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Xuepeng Zhao
Keyword(s):  
Optical Thickness ◽  
Seasonal Variations ◽  
Confidence Level ◽  
Aerosol Optical Thickness ◽  
Positive Trend ◽  
Air Pollution Control ◽  
Industrialized Countries ◽  
Climate Data ◽  
Climate Data Record ◽  
Very High

Nearly 30-year aerosol optical thickness (AOT) climate data record (CDR) derived from the operational satellite observations of National Ocean and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) is used to study the AOT trends over seventeen megacities in the coast zone (MCCZ). Linear trends are derived from monthly and seasonal mean AOT in the past three decades and used in the analysis. The results indicate the following: (1) AOT around a MCCZ in fast developing countries has relatively high value and a positive trend with a confidence level generally above 95%; (2) AOT around a MCCZ in industrialized countries has relatively low value and a negative trend with a confidence level generally above 95%; (3) AOT values and their trends show distinct seasonal variations in MCCZ, which can be explained somewhat by the seasonal variations of meteorological conditions. AOT trend is an effective index for examining the efficacy of air pollution control policies implemented for these megacities.

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 The Impact of Climate Change on the Water Balance of Oil Sands Reclamation Covers and Natural Soil Profiles

2018 ◽  
Vol 19 (11) ◽  
pp. 1731-1752 ◽  
Author(s):  
Md. Shahabul Alam ◽  
S. Lee Barbour ◽  
Amin Elshorbagy ◽  
Mingbin Huang
Keyword(s):  
Oil Sands ◽  
Weather Generator ◽  
First Century ◽  
Soil Profiles ◽  
Percentage Increase ◽  
Climate Data ◽  
Historical Climate ◽  
Twenty First Century ◽  
The Impact

Abstract The design of reclamation soil covers at oil sands mines in northern Alberta, Canada, has been conventionally based on the calibration of soil–vegetation–atmosphere transfer (SVAT) models against field monitoring observations collected over several years, followed by simulations of long-term performance using historical climate data. This paper evaluates the long-term water balances for reclamation covers on two oil sands landforms and three natural coarse-textured forest soil profiles using both historical climate data and future climate projections. Twenty-first century daily precipitation and temperature data from CanESM2 were downscaled based on three representative concentration pathways (RCPs) employing a stochastic weather generator [Long Ashton Research Station Weather Generator (LARS-WG)]. Relative humidity, wind speed, and net radiation were downscaled using the delta change method. Downscaled precipitation and estimated potential evapotranspiration were used as inputs to simulate soil water dynamics using physically based models. Probability distributions of growing season (April–October) actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods show that AET and NP at all sites are expected to increase throughout the twenty-first century regardless of RCP, time period, and soil profile. Greater increases in AET and NP are projected toward the end of the twenty-first century. The increases in future NP at the two reclamation covers are larger (as a percentage increase) than at most of the natural sites. Increases in NP will result in greater water yield to surface water and may accelerate the rate at which chemical constituents contained within mine waste are released to downstream receptors, suggesting these potential changes need to be considered in mine closure designs.

scholarly journals Impact of satellite viewing swath width on global and regional aerosol optical thickness statistics and trends

2013 ◽  
Vol 6 (6) ◽  
pp. 10117-10163 ◽  
Author(s):  
P. R. Colarco ◽  
R. A. Kahn ◽  
L. A. Remer ◽  
R. C. Levy
Keyword(s):  
Optical Thickness ◽  
Statistical Significance ◽  
Sampling Strategy ◽  
Aerosol Optical Thickness ◽  
Mean Values ◽  
Sampling Artifacts ◽  
Aerosol Loading ◽  
Decadal Scale ◽  
Along Track ◽  
The Impact

Abstract. We use the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite aerosol optical thickness (AOT) product to assess the impact of reduced swath width on global and regional AOT statistics and trends. Ten different sampling strategies are employed, in which the full MODIS dataset is sub-sampled with various narrow-swath (~400–800 km) and curtain-like (~10 km) along-track configurations. Although view-angle artifacts in the MODIS AOT retrieval confound direct comparisons between averages derived from different sub-samples, careful analysis shows that with many portions of the Earth essentially unobserved, the AOT statistics of these sub-samples exhibit significant regional and seasonal biases. These AOT spatial sampling artifacts comprise up to 60% of the full-swath AOT value under moderate aerosol loading, and can be as large as 0.1 in some regions under high aerosol loading. Compared to full-swath observations, narrower swaths exhibit a reduced ability to detect AOT trends with statistical significance, and for curtain-like sampling we do not find any statistically significant decadal-scale trends at all. An across-track sampling strategy obviates the MODIS view angle artifact, and its mean AOT converges to the full-swath mean values for sufficiently coarse spatial and temporal aggregation. Nevertheless, across-track sampling has significant seasonal-regional sampling artifacts, leading to biases comparable to the curtain-like along-track sampling, lacks sufficient coverage to assign statistical significance to aerosol trends, and is not achievable with an actual narrow-swath or curtain-like instrument. These results suggest that future aerosol satellite missions having significantly less than full-swath viewing are unlikely to sample the true AOT distribution well enough to determine decadal-scale trends or to obtain the statistics needed to reduce uncertainty in aerosol direct forcing of climate.

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 Performance Evaluation of Satellite-Based Rainfall Products over Nigeria

Climate ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 103
Author(s):  
Kingsley N. Ogbu ◽  
Nina Rholan Hounguè ◽  
Imoleayo E. Gbode ◽  
Bernhard Tischbein
Keyword(s):  
Agricultural Development ◽  
Rain Gauge ◽  
Local Economy ◽  
Climate Data ◽  
Climatic Zones ◽  
Rainfall Characteristics ◽  
Precipitation Estimation ◽  
Climate Data Record ◽  
Local Rainfall

Understanding the variability of rainfall is important for sustaining rain-dependent agriculture and driving the local economy of Nigeria. Paucity and inadequate rain gauge network across Nigeria has made satellite-based rainfall products (SRPs), which offer a complete spatial and consistent temporal coverage, a better alternative. However, the accuracy of these products must be ascertained before use in water resource developments and planning. In this study, the performances of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT), were evaluated to investigate their ability to reproduce long term (1983–2013) observed rainfall characteristics derived from twenty-four (24) gauges in Nigeria. Results show that all products performed well in terms of capturing the observed annual cycle and spatial trends in all selected stations. Statistical evaluation of the SRPs performance show that CHIRPS agree more with observations in all climatic zones by reproducing the local rainfall characteristics. The performance of PERSIANN and TAMSAT, however, varies with season and across the climatic zones. Findings from this study highlight the benefits of using SRPs to augment or fill gaps in the distribution of local rainfall data, which is critical for water resources planning, agricultural development, and policy making.

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 The Cloud_cci simulator for the ESA Cloud_cci climate data record and its application to a global and a regional climate model

2018 ◽  
Author(s):  
Salomon Eliasson ◽  
Karl Göran Karlsson ◽  
Erik van Meijgaard ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Climate Data ◽  
European Continent ◽  
Cloud Properties ◽  
Data Record ◽  
The Impact ◽  
Climate Data Record

Abstract. The Cloud_cci satellite simulator has been developed to enable comparisons between the Cloud_cci Climate Data Record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth Global Climate Model as well as the RACMO Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in Liquid Water Path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces Total Cloud Fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian sub-continent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean, but lower than retrieved over the European continent, where in addition the Cloud Top Pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modelled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

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