Exploring the potential of vegetation information for improving weather forecast performance

2021 ◽  
Author(s):  
Melissa Ruiz ◽  
Sungmin Oh ◽  
Rene Orth ◽  
Gianpaolo Balsamo
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Weather Forecasting ◽  
Forecast Accuracy ◽  
Weather Forecast ◽  
Spatial And Temporal Variation ◽  
Forecast Errors ◽  
Forecast Performance ◽  
Forecast Models ◽  
Moderate Resolution Imaging Spectroradiometer

<p>The quality of weather forecasts is continuously improving for decades. However, increases in forecast skills have slowed down in recent years. This highlights the importance of exploring new avenues towards future forecast system improvements. Until now, (near) real-time information on vegetation anomalies is not used in most forecasting models. Addressing this gap, we explore the potential of the vegetation state for explaining the spatial and temporal variation in forecast accuracy globally across climate regions, seasons, and vegetation types. For this purpose, we employ re-forecasts from the European Centre of Medium-Range Weather Forecasting (ECMWF) and infer the vegetation status through the Enhanced Vegetation Index derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite observations during the 2000-2019 period. In particular, we focus on land surface variables such as evaporation and temperature to study the relationship between forecast errors and vegetation anomalies.</p><p>The results show a stronger correlation between forecast errors and vegetation anomalies in semi-arid and sub-humid regions during the growing season, which highlights that vegetation information has the potential to help advance weather forecast performance. To put these results into perspective, we will further perform a multivariate analysis to determine the relative roles of vegetation, hydrology and climate in explaining weather forecast errors. Thereby, our results can inform the future development of weather forecast models and underlying data assimilation schemes.</p>

scholarly journals A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Guang Yang ◽  
Yuntao Ma ◽  
Jiaqi Hu
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Extraction Methods ◽  
Average Error ◽  
Geographic Coordinate System ◽  
Optimal Thresholds ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

Predictive Ability of Business Cycle Indicators under Test

2011 ◽  
Vol 231 (1) ◽  
Author(s):  
Kai Carstensen ◽  
Klaus Wohlrabe ◽  
Christina Ziegler
Keyword(s):  
Predictive Ability ◽  
Optimal Choice ◽  
Good Choice ◽  
Economic Stress ◽  
Forecast Errors ◽  
Business Climate ◽  
Forecast Performance ◽  
Leading Indicator ◽  
Forecast Models ◽  
Climate Indicator

SummaryIn this paper we assess the information content of seven widely cited early indicators for the euro area with respect to forecasting area-wide industrial production. To this end, we use various tests that are designed to compare competing forecast models. In addition to the standard Diebold-Mariano test, we employ tests that account for specific problems typically encountered in forecast exercises. Specifically, we pay attention to nested model structures, we alleviate the problem of data snooping arising from multiple pairwise testing, and we analyze the structural stability in the relative forecast performance of one indicator compared to a benchmark model. Moreover, we consider loss functions that overweight forecast errors in booms and recessions to check-whether a specific indicator that appears to be a good choice on average is also preferable in times of economic stress. We find that none of this indicators uniformly dominates all its competitors. The optimal choice rather depends on the specific forecast situation and the loss function of the user. For 1-month forecasts the business climate indicator of the European Commission and the OECD composite leading indicator generally work well, for 6-month forecasts the OECD composite leading indicator performs very good by all criteria, and for 12-month forecasts the FAZ-Euro indicator published by the Frankfurter Allgemeine Zeitung is the only one that can beat the benchmark AR(1) model.

Numerical Weather Forecast Post-processing with Ensemble Learning and Transfer Learning

2020 ◽  
Author(s):  
Yuwen Chen ◽  
Xiaomeng Huang
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Ensemble Learning ◽  
Weather Forecasting ◽  
Weather Forecast ◽  
Processing Temperature ◽  
Forecast Errors ◽  
Post Processing ◽  
Numerical Weather ◽  
One Year

<p>Statistical approaches have been used for decades to augment and interpret numerical weather forecasts. The emergence of artificial intelligence algorithms has provided new perspectives in this field, but the extension of algorithms developed for station networks with rich historical records to include newly-built stations remains a challenge. To address this, we design a framework that combines two machine learning methods: temperature prediction based on ensemble of multiple machine learning models and transfer learning for newly-built stations. We then evaluate this framework by post-processing temperature forecasts provided by a leading weather forecast center and observations from 301 weather stations in China. Station clustering reduces forecast errors by 24.4% averagely, while transfer learning improves predictions by 13.4% for recently-built sites with only one year of data available. This work demonstrates how ensemble learning and transfer learning can be used to supplement weather forecasting.</p><p></p>

scholarly journals Land surface parameter optimisation through data assimilation: the adJULES system

2016 ◽  
Author(s):  
Nina M. Raoult ◽  
Tim E. Jupp ◽  
Peter M. Cox ◽  
Catherine M. Luke
Keyword(s):  
Land Surface ◽  
Gross Primary Production ◽  
Land Surface Model ◽  
Weather Forecast ◽  
Surface Model ◽  
Similar Reduction ◽  
Land Surface Parameter ◽  
Forecast Models ◽  
Estimation System ◽  
The Uk

Abstract. Land-surface models (LSMs) are crucial components of the Earth System Models (ESMs) which are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. In this study, JULES is automatically differentiated using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed, to search for locally optimum parameter sets by calibrating against observations. This paper describes adJULES and demonstrates its ability to improve the model-data fit using eddy covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the 5 Plant Functional Types (PFTS) in JULES. The optimised PFT-specific parameters improve the performance of JULES over 90 % of the sites used in the study, a third of which give similar reduction in errors as site specific optimisations. The new improved parameter set for JULES is presented along with the associated uncertainties for each parameter.

scholarly journals Advances in the WRF model for convection-resolving forecasting

2006 ◽  
Vol 7 ◽  
pp. 25-29 ◽  
Author(s):  
J. B. Klemp
Keyword(s):  
Wrf Model ◽  
Forecast Accuracy ◽  
Weather Forecast ◽  
Cumulus Parameterization ◽  
Weather Research And Forecasting ◽  
Convective Systems ◽  
Convective Processes ◽  
Forecast Models ◽  
Good Potential ◽  
Mesh Spacing

Abstract. The Weather Research and Forecasting (WRF) Model has been designed to be an efficient and flexible simulation system for use across a broad range of weather-forecast and idealized-research applications. Of particular interest is the use of WRF in nonhydrostatic applications in which moist-convective processes are treated explicitly, thereby avoiding the ambiguities of cumulus parameterization. To evaluate the capabilities of WRF for convection-resolving applications, real-time forecasting experiments have been conducted with 4 km horizontal mesh spacing for both convective systems in the central U.S. and for hurricanes approaching landfall in the southeastern U.S. These forecasts demonstrate a good potential for improving the forecast accuracy of the timing and location of these systems, as well as providing more detailed information on their structure and evolution that is not available in current coarser resolution operational forecast models.

scholarly journals Detection of Decreasing Vegetation Cover Based on Empirical Orthogonal Function and Temporal Unmixing Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Di Xu ◽  
Ruishan Chen ◽  
Xiaoshi Xing ◽  
Wenpeng Lin
Keyword(s):  
Empirical Orthogonal Function ◽  
Land Surface ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Orthogonal Function ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Hydrological Cycles ◽  
Spectral Mixture

Vegetation plays an important role in the energy exchange of the land surface, biogeochemical cycles, and hydrological cycles. MODIS (MODerate-resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) is considered as a quantitative indicator for examining dynamic vegetation changes. This paper applied a new method of integrated empirical orthogonal function (EOF) and temporal unmixing analysis (TUA) to detect the vegetation decreasing cover in Jiangsu Province of China. The empirical orthogonal function (EOF) statistical results provide vegetation decreasing/increasing trend as prior information for temporal unmixing analysis. Temporal unmixing analysis (TUA) results could reveal the dominant spatial distribution of decreasing vegetation. The results showed that decreasing vegetation areas in Jiangsu are distributed in the suburbs and newly constructed areas. For validation, the vegetation’s decreasing cover is revealed by linear spectral mixture from Landsat data in three selected cities. Vegetation decreasing areas pixels are also calculated from land use maps in 2000 and 2010. The accuracy of integrated empirical orthogonal function and temporal unmixing analysis method is about 83.14%. This method can be applied to detect vegetation change in large rapidly urbanizing areas.

scholarly journals Investigation of Spatial and Temporal Changes in the Land Surface Albedo for the Entire Chinese Territory

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 362
Author(s):  
Jihui Yuan
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Global Climate ◽  
Temporal Changes ◽  
Spatial And Temporal Changes ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Satellite

Currently, global climate change (GCC) and the urban heat island (UHI) phenomena are becoming serious problems, partly due to the artificial construction of the land surface. When sunlight reaches the land surface, some of it is absorbed and some is reflected. The state of the land surface directly affects the surface albedo, which determines the magnitude of solar radiation reflected by the land surface in the daytime. In order to better understand the spatial and temporal changes in surface albedo, this study investigated and analyzed the surface albedo from 2000 to 2016 (2000, 2008, and 2016) in the entire Chinese territory, based on the measurement database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, aboard NASA’s Terra satellite. It was shown that the Northeast China exhibited the largest decline in surface albedo and North China showed the largest rising trend of surface albedo from 2000 to 2016. The correlation between changes in surface albedo and the Normalized Difference Vegetation Index (NDVI) indicated that the change trend of surface albedo was opposite to that of NDVI. In addition, in order to better understand the distribution of surface albedo in the entire Chinese territory, the classifications of surface albedo in three years (2000, 2008, and 2016) were implemented using five classification methods in this study.

Changes in land surface and weather forecasting

2020 ◽  
Author(s):  
Iman Rousta ◽  
Haraldur Ólafsson
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Forecasting ◽  
Positive Trend ◽  
Thermally Driven ◽  
The Impact ◽  
Local Weather

<p>The Normalized Difference Vegetation Index (NDVI) has been retrieved and analyzed for Iceland.  There are only limited trends in the total integrated NDVI in the period 2001 - 2018.  However, there is a positive trend in recent decades in the occurrence of signal corresponding to woodland and forests.   Locally, there may however be great changes; some small deserts have turned green and systematic planting of trees in certain regions is well detectable. The impact, and the driver of these changes are discussed in the context of climate and the implication for thermally driven weather systems and local weather forecasting is explained. </p>

An Empirical Analysis of Forecast Performance of the GDP Growth in India

2019 ◽  
Vol 20 (2) ◽  
pp. 368-386 ◽  
Author(s):  
Monika Gupta ◽  
Mohammad Haris Minai
Keyword(s):  
Forecast Error ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Gdp Growth ◽  
Forecast Errors ◽  
Forecast Performance ◽  
Reserve Bank ◽  
Rules Of Thumb ◽  
Actual Outcome ◽  
High Degree

This article evaluates the accuracy of a forecast based on the properties of the forecast error. To measure how close the predictions of GDP growth are to the actual outcome in India, we have calculated three measures of forecast accuracy: mean absolute error (MAE), root mean square error (RMSE) and Theil’s U statistic. To evaluate the performance of the forecasts, we have compared them with naive forecast and common rules of thumb, using moving averages (MAs) as rules of thumb. The results are inconclusive regarding biasedness and also inefficient. Further, the forecasts have a high degree of correlation among themselves. The findings of forecast errors suggest that the performance of Reserve Bank of India (RBI) forecasts is favourable compared to other organizations, as well as with respect to the general international standard.

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