Impact of MODIS land cover data on surface predictions over Taiwan in the FV3GFS model

2020 ◽  
Author(s):  
Ling-Feng Hsiao ◽  
Feng-Ju Wang
Keyword(s):  
Land Cover ◽  
Weather Prediction ◽  
Wrf Model ◽  
Bias Reduction ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Significant Difference ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Environmental Prediction ◽  
Data Elements

<p>The global numerical weather prediction (NWP) system based on the FV3GFS model jointly developed by U.S. National Centers for Environmental Prediction (NCEP) and Geophysical Fluid Dynamics Laboratory (GFDL) has been implemented in the Taiwan’s Central Weather Bureau (CWB) forecast system for the next generation global NWP operations. Currently, NCEP FV3GFS model provides land use dataset retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. The MODIS 20-category data is composed of roughly 12 km resolution data elements. However, the modified of the 20-class MODIS land cover dataset with a resolution of 500 m which defined by the International Geosphere-Biosphere Program (IGBP) is provided by WRF model. A significant difference between these two datasets is MODIS data from NCEP FV3GFS as being extremely urbanized in western Taiwan. In a case of weaker synoptic-scale forcing, the modified MODIS land cover dataset from WRF model result in a larger improvement in 2-m temperature and 2-m mixing ratio when compare to the surface observations over Taiwan. The reason results from the overestimation of urban area in NCEP FV3GFS model, which contains previous and low-resolution MODIS dataset. Moreover, there is a bias reduction in 10-m wind speed as well as thermal effects. The detailed results will be presented in the conference.</p>

scholarly journals Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch)

2018 ◽  
Vol 11 (3) ◽  
pp. 989-1008 ◽  
Author(s):  
Klaus Klingmüller ◽  
Swen Metzger ◽  
Mohamed Abdelkader ◽  
Vlassis A. Karydis ◽  
Georgiy L. Stenchikov ◽  
...  
Keyword(s):  
Land Cover ◽  
Atmospheric Chemistry ◽  
Input Data ◽  
Climate Model ◽  
Weather Prediction ◽  
Clay Fraction ◽  
Max Planck ◽  
Data Set ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. To improve the aeolian dust budget calculations with the global ECHAM/MESSy atmospheric chemistry–climate model (EMAC), which combines the Modular Earth Submodel System (MESSy) with the ECMWF/Hamburg (ECHAM) climate model developed at the Max Planck Institute for Meteorology in Hamburg based on a weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF), we have implemented new input data and updates of the emission scheme. The data set comprises land cover classification, vegetation, clay fraction and topography. It is based on up-to-date observations, which are crucial to account for the rapid changes of deserts and semi-arid regions in recent decades. The new Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover and vegetation data are time dependent, and the effect of long-term trends and variability of the relevant parameters is therefore considered by the emission scheme. All input data have a spatial resolution of at least 0.1∘ compared to 1∘ in the previous version, equipping the model for high-resolution simulations. We validate the updates by comparing the aerosol optical depth (AOD) at 550 nm wavelength from a 1-year simulation at T106 (about 1.1∘) resolution with Aerosol Robotic Network (AERONET) and MODIS observations, the 10 µm dust AOD (DAOD) with Infrared Atmospheric Sounding Interferometer (IASI) retrievals, and dust concentration and deposition results with observations from the Aerosol Comparisons between Observations and Models (AeroCom) dust benchmark data set. The update significantly improves agreement with the observations and is therefore recommended to be used in future simulations.

scholarly journals Global analysis of radiative forcing from fire-induced shortwave albedo change

2015 ◽  
Vol 12 (2) ◽  
pp. 557-565 ◽  
Author(s):  
G. López-Saldaña ◽  
I. Bistinas ◽  
J. M. C. Pereira
Keyword(s):  
Land Surface ◽  
Radiative Forcing ◽  
Global Analysis ◽  
Weather Prediction ◽  
Global Scale ◽  
Burned Area ◽  
Albedo Change ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Environmental Prediction ◽  
Area Product

Abstract. Land surface albedo, a key parameter to derive Earth's surface energy balance, is used in the parameterization of numerical weather prediction, climate monitoring and climate change impact assessments. Changes in albedo due to fire have not been fully investigated on a continental and global scale. The main goal of this study, therefore, is to quantify the changes in instantaneous shortwave albedo produced by biomass burning activities and their associated radiative forcing. The study relies on the MODerate-resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned-area product to create an annual composite of areas affected by fire and the MCD43C2 bidirectional reflectance distribution function (BRDF) albedo snow-free product to compute a bihemispherical reflectance time series. The approximate day of burning is used to calculate the instantaneous change in shortwave albedo. Using the corresponding National Centers for Environmental Prediction (NCEP) monthly mean downward solar radiation flux at the surface, the global radiative forcing associated with fire was computed. The analysis reveals a mean decrease in shortwave albedo of −0.014 (1σ = 0.017), causing a mean positive radiative forcing of 3.99 Wm−2 (1σ = 4.89) over the 2002–20012 time period in areas affected by fire. The greatest drop in mean shortwave albedo change occurs in 2002, which corresponds to the highest total area burned (378 Mha) observed in the same year and produces the highest mean radiative forcing (4.5 Wm−2). Africa is the main contributor in terms of burned area, but forests globally give the highest radiative forcing per unit area and thus give detectable changes in shortwave albedo. The global mean radiative forcing for the whole period studied (~0.0275 Wm−2) shows that the contribution of fires to the Earth system is not insignificant.

scholarly journals The Spatial and Temporal Land Cover Patterns of the Qazaly Irrigation Zone in 2003–2018: The Case of Syrdarya River’s Lower Reaches, Kazakhstan

2019 ◽  
Vol 11 (15) ◽  
pp. 4035 ◽  
Author(s):  
Kanat Samarkhanov ◽  
Jilili Abuduwaili ◽  
Alim Samat ◽  
Gulnura Issanova
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Algorithm ◽  
Aral Sea ◽  
Landsat 8 ◽  
Spatial And Temporal Patterns ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Cover Maps

In this study, the spatial and temporal patterns of the land cover were monitored within the Qazaly irrigation zone located in the deltaic zone of the Syrdarya river in the surroundings of the former Aral Sea. A 16-day MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua NDVI (Normalized Difference Vegetation Index) data product with a spatial resolution of 250 meters was used for this purpose, covering the period between 2003 and 2018. Field survey results obtained in 2018 were used to build a sample dataset. The random forests supervised classification machine learning algorithm was used to map land cover, which produced good results with an overall accuracy of about 0.8. Statistics on land cover change were calculated and analyzed. The correctness of obtained classes was checked with Landsat 8 (OLI, The Operational Land Imager) images. Detailed land cover maps, including rice cropland, were derived. During the observation period, the rice croplands increased, while the generally irrigated area decreased.

scholarly journals Validation of Satellite-Based Objective Overshooting Cloud-Top Detection Methods Using CloudSat Cloud Profiling Radar Observations

2012 ◽  
Vol 51 (10) ◽  
pp. 1811-1822 ◽  
Author(s):  
Kristopher M. Bedka ◽  
Richard Dworak ◽  
Jason Brunner ◽  
Wayne Feltz
Keyword(s):  
Weather Prediction ◽  
Convective Cloud ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Detection Methods ◽  
Spatial Gradients ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractTwo satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11-μm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD > 0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions.

scholarly journals Effect of Using Multi-Year LULC and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

2021 ◽  
Author(s):  
Ibrahim Olayode Busari ◽  
Mehmet Cüneyd Demirel ◽  
Alice Newton
Keyword(s):  
Land Cover ◽  
Potential Evapotranspiration ◽  
Hydrologic Model ◽  
Parameter Uncertainties ◽  
Area Index ◽  
Distributed Hydrologic Model ◽  
Land Cover Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

This study explores the use of satellite-based LULC (Land Use / Land Cover) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

scholarly journals Fire Frequency and Related Land-Use and Land-Cover Changes in Indonesia’s Peatlands

2019 ◽  
Vol 12 (1) ◽  
pp. 5 ◽  
Author(s):  
Yenni Vetrita ◽  
Mark A. Cochrane
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Fire Regimes ◽  
Fire Frequency ◽  
Burned Area ◽  
Swamp Forest ◽  
Lulc Change ◽  
Surface Areas ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Indonesia’s converted peatland areas have a well-established fire problem, but limited studies have examined the frequency with which they are burning. Here, we quantify fire frequency in Indonesia’s two largest peatland regions, Sumatra and Kalimantan, during 2001–2018. We report, annual areas burned, total peatland area affected by fires, amount of recurrent burning and associations with land-use and land-cover (LULC) change. We based these analyses on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua combined burned area and three Landsat-derived LULC maps (1990, 2007, and 2015) and explored relationships between burning and land-cover types. Cumulative areas burned amounted nearly half of the surface areas of Sumatra and Kalimantan but were concentrated in only ~25% of the land areas. Although peatlands cover only 13% of Sumatra and Kalimantan, annual percentage of area burning in these areas was almost five times greater than in non-peatlands (2.8% vs. 0.6%) from 2001 to 2018. Recurrent burning was more prominent in Kalimantan than Sumatra. Average fire-return intervals (FRI) in peatlands of both regions were short, 28 and 45 years for Kalimantan and Sumatra, respectively. On average, forest FRI were less than 50 years. In non-forest areas, Kalimantan had shorter average FRI than Sumatra (13 years vs. 40 years), with ferns/low shrub areas burning most frequently. Our findings highlight the significant influence of LULC change in altering fire regimes. If prevalent rates of burning in Indonesia’s peatlands are not greatly reduced, peat swamp forest will disappear from Sumatra and Kalimantan in the coming decades.

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