Estimating the effective spatio-temporal resolution of integrated water vapor trends based on VLBI, GNSS, and weather model data

2021 ◽  
Author(s):  
Fikri Bamahry ◽  
Kyriakos Balidakis ◽  
Robert Heinkelmann ◽  
Harald Schuh
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Temporal Resolution ◽  
Weather Prediction ◽  
Classification Systems ◽  
Spatial Covariance ◽  
Covariance Functions ◽  
Area Of Interest ◽  
Significant Difference ◽  
Spatio Temporal

<p>How far apart can two space geodetic sites be located to consider the integrated water vapor (hereinafter IWV) trends as equal, from a statistical viewpoint? How to do efficient feature selection with a given IWV time series? To address these questions, we utilize spatio-temporal variations of long-term IWV trends that were estimated employing very long baseline interferometry (VLBI), Global Navigation Satellite Systems (GNSS), and numerical weather prediction data (ERA5 reanalysis). We estimate coefficients for several spatial covariance functions; Hirvonen's model proves to be the most precise for our area of interest, Greater Europe. We find that the effective spatial resolution is around 56 km (for error level (p) < 0.05). Our investigations indicate that among else, altitude and proximity to the ocean are key factors affecting the IWV trend decorrelation lengths. We find good agreement between the spatially varying decorrelation lengths and established climate classification systems such as the latest Köppen-Geiger model. Moreover, the IWV trend variation as a function of data span and temporal resolution has been investigated. We find that varying the temporal resolution from one hour up to 30 days does not yield a statistically significant difference (p < 0.05) in the IWV trend and its uncertainty, provided the inherent autocorrelation is factored in and the data span remains. We also find that given the IWV time series length, the spread calculated from the estimated trends varying the start point of the time series, follows an exponential decrease <em>σ</em>(Δ<em>t</em>) = 22Δ<em>t</em> <sup>-1.7</sup> + 0.008.</p>

scholarly journals Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade

2010 ◽  
Vol 23 (7) ◽  
pp. 1675-1695 ◽  
Author(s):  
Sibylle Vey ◽  
Reinhard Dietrich ◽  
Axel Rülke ◽  
Mathias Fritsche ◽  
Peter Steigenberger ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Seasonal Cycle ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Atmospheric Temperature ◽  
Interannual Variations ◽  
Seasonal Signals ◽  
The Tropics

Abstract In contrast to previous studies validating numerical weather prediction (NWP) models using observations from the global positioning system (GPS), this paper focuses on the validation of seasonal and interannual variations in the water vapor. The main advantage of the performed validation is the independence of the GPS water vapor estimates compared to studies using water vapor datasets from radiosondes or satellite microwave radiometers that are already assimilated into the NWP models. Tropospheric parameters from a GPS reanalysis carried out in a common project of the Technical Universities in Munich and Dresden were converted into precipitable water (PW) using surface pressure observations from the WMO and mean atmospheric temperature data from ECMWF. PW time series were generated for 141 globally distributed GPS sites covering the time period from the beginning of 1994 to the end of 2004. The GPS-derived PW time series were carefully examined for their homogeneity. The validation of the NWP model from NCEP shows that the differences between the modeled and observed PW values are time dependent. In addition to establishing a long-term mean, this study also validates the seasonal cycle and interannual variations in the PW. Over Europe and large parts of North America the seasonal cycle and the interannual variations in the PW from GPS and NCEP agree very well. The results reveal a submillimeter accuracy of the GPS-derived PW anomalies. In the regions mentioned above, NCEP provides a highly accurate database for studies of long-term changes in the atmospheric water vapor. However, in the Southern Hemisphere large differences in the seasonal signals and in the PW anomalies were found between GPS and NCEP. The seasonal signal of the PW is underestimated by NCEP in the tropics and in Antarctica by up to 40% and 25%, respectively. Climate change studies based on water vapor data from NCEP should consider the large uncertainties in the analysis when interpreting these data, especially in the tropics.

scholarly journals EuLerian Identification of Ascending air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part II: Model application to different data sets

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian Michael Grams ◽  
Annika Oertel ◽  
Moritz Pickl
Keyword(s):  
Temporal Resolution ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Future Research ◽  
Data Sets ◽  
Ensemble Forecasts ◽  
Numerical Weather ◽  
Spatio Temporal ◽  
Air Streams

Abstract. Warm conveyor belts (WCBs) affect the atmospheric dynamics in midlatitudes and are highly relevant for total and extreme precipitation in many parts of the extratropics. Thus, these air streams and their effect on midlatitude weather should be well represented in numerical weather prediction (NWP) and climate models. This study applies newly developed convolutional neural network (CNN) models which allow the identification of footprints of WCB inflow, ascent, and outflow from a limited number of predictor fields at comparably low spatio-temporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different data sets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart which is most frequently used to objectively identify WCBs but requires data at higher spatio-temporal resolution which is often not available and is computationally more expensive. First, an application to reanalyses reveals that the well-known relationship between WCB ascent and extratropical cyclones as well as between WCB outflow and blocking anticyclones is also found for WCB footprints identified with the CNN models. Second, the application to Japanese 55-year reanalyses shows how the CNN models may be used to identify erroneous predictor fields that deteriorate the models' reliability. Third, a verification of WCBs in operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for three Northern Hemisphere winters reveals systematic biases over the North Atlantic with both the trajectory-based approach and the CNN models. The ensemble forecasts' skill tends to be lower when being evaluated with the trajectory approach due to the fine-scale structure of WCB footprints in comparison to the rather smooth CNN-based WCB footprints. A final example demonstrates the applicability of the CNN models to a convection permitting simulation with the ICOsahedral Nonhydrostatic (ICON) NWP model. Our study illustrates that deep learning methods can be used efficiently to support process-oriented understanding of forecast error and model biases, and opens numerous directions for future research.

scholarly journals Rubber Identification Based on Blended High Spatio-Temporal Resolution Optical Remote Sensing Data: A Case Study in Xishuangbanna

2019 ◽  
Vol 11 (5) ◽  
pp. 496 ◽  
Author(s):  
Shupeng Gao ◽  
Xiaolong Liu ◽  
Yanchen Bo ◽  
Zhengtao Shi ◽  
Hongmin Zhou
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Temporal Resolution ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Rubber Plantation ◽  
Optical Remote Sensing ◽  
Rubber Plantations ◽  
Sensing Data ◽  
Spatio Temporal

As an important economic resource, rubber has rapidly grown in Xishuangbanna of Yunnan Province, China, since the 1990s. Tropical rainforests have been replaced by extensive rubber plantations, which has resulted in ecological problems such as the loss of biodiversity and local water shortages. It is vitally important to accurately map the rubber plantations in this region. Although several rubber mapping methods have been proposed, few studies have investigated methods based on optical remote sensing time series data with high spatio-temporal resolution due to the cloudy and foggy weather conditions in this area. This study presented a rubber plantation identification method that used spatio-temporal optical remote sensing data fusion technology to obtain vegetation index data at high spatio-temporal resolution within the optical remote sensing window in Xishuangbanna. The analysis of the proposed method shows that (1) fused optical remote sensing data with high spatio-temporal resolution could map the rubber distribution with high accuracy (overall accuracy of up to 89.51% and kappa of 0.86). (2) Fused indices have high R2 (R2 greater than 0.8, where R is the correlation coefficient) with the indices that were derived from the Landsat observed data, which indicates that fusion results are dependable. However, the fusion accuracy is affected by terrain factors including elevation, slope, and slope aspects. These factors have obvious negative effects on the fusion accuracy of high spatio-temporal resolution optical remote sensing data: the highest fusion accuracy occurred in areas with elevations between 1201 and 1400 m.a.s.l., and the lowest accuracy occurred in areas with elevations less than 600 m.a.s.l. For the 5 fused time series indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and tasseled cap angle (TCA)), the fusion accuracy decreased with increasing slope, and increasing slope had the least impact on the EVI, but the greatest negative impact on the NDVI; the slope aspect had a limited influence on the fusion accuracies of the 5 time series indices, but fusion accuracy was lowest on the northwest slope. (3) EVI had the highest accuracy of rubber plantation classification among the 5 time series indices, and the overall classification accuracies of the time series EVI for the four different years (2000, 2005, 2010, and 2015) reached 87.20% (kappa 0.82), 86.91% (kappa 0.81), 88.85% (kappa 0.84), and 89.51% (kappa 0.86), respectively. The results indicate that the method is a promising approach for rubber plantation mapping and the detection of changes in rubber plantations in this tropical area.

scholarly journals Classification of Gaussian spatio-temporal data with stationary separable covariances

2021 ◽  
Vol 26 (2) ◽  
pp. 363-374
Author(s):  
Marta Karaliutė ◽  
Kęstutis Dučinskas
Keyword(s):  
Gaussian Random Field ◽  
Error Rates ◽  
Small Error ◽  
Classification Rule ◽  
Temporal Data ◽  
Spatial Covariance ◽  
Covariance Functions ◽  
Novel Approach ◽  
Spatio Temporal

The novel approach to classification of spatio-temporal data based on Bayes discriminant functions is developed. We focus on the problem of supervised classifying of the spatiotemporal Gaussian random field (GRF) observation into one of two classes specified by different drift parameters, separable nonlinear covariance functions and nonstationary label field. The performance of proposed classification rule is validated by the values of local Bayes and empirical error rates realized by leave one out procedure. A simulation study for spatial covariance functions belonging to powered-exponential family and temporal covariance functions of AR(1) models is carried out. The influence of the values of spatial and temporal covariance parameters to error rates for several label field models are studied. The results showed that the proposed classification methodology can be applied successfully in  practice with small error rates and can be a useful tool for discriminant analysis of spatio-temporal data.

scholarly journals A Spatio-temporal disaggregation method to derive time series of Normalized Difference Vegetation Index and Land Surface Temperature at fine spatial resolution

2014 ◽  
Vol XL-8 ◽  
pp. 1397-1401
Author(s):  
V. M. Bindhu ◽  
B. Narasimhan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Energy Balance ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Fine Spatial Resolution ◽  
Temporal Disaggregation ◽  
Energy Balance Models ◽  
Spatio Temporal ◽  
Fine Resolution

Estimation of evapotranspiration (ET) from remote sensing based energy balance models have evolved as a promising tool in the field of water resources management. Performance of energy balance models and reliability of ET estimates is decided by the availability of remote sensing data at high spatial and temporal resolutions. However huge tradeoff in the spatial and temporal resolution of satellite images act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. Hence a need exists to derive finer resolution data from the available coarse resolution imagery, which could be applied to deliver ET estimates at scales to the range of individual fields. The current study employed a spatio-temporal disaggregation method to derive fine spatial resolution (60 m) images of NDVI by integrating the information in terms of crop phenology derived from time series of MODIS NDVI composites with fine resolution NDVI derived from a single AWiFS data acquired during the season. The disaggregated images of NDVI at fine resolution were used to disaggregate MODIS LST data at 960 m resolution to the scale of Landsat LST data at 60 m resolution. The robustness of the algorithm was verified by comparison of the disaggregated NDVI and LST with concurrent NDVI and LST images derived from Landsat ETM+. The results showed that disaggregated NDVI and LST images compared well with the concurrent NDVI and LST derived from ETM+ at fine resolution with a high Nash Sutcliffe Efficiency and low Root Mean Square Error. The proposed disaggregation method proves promising in generating time series of ET at fine resolution for effective water management.

scholarly journals Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM model

2015 ◽  
Vol 19 (6) ◽  
pp. 2717-2736 ◽  
Author(s):  
A. Kuentz ◽  
T. Mathevet ◽  
J. Gailhard ◽  
B. Hingray
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Air Temperature ◽  
Regional Scale ◽  
Precipitation Time Series ◽  
Temporal Scales ◽  
Spatial Homogeneity ◽  
Temperature And Precipitation ◽  
Spatio Temporal

Abstract. Efforts to improve the understanding of past climatic or hydrologic variability have received a great deal of attention in various fields of geosciences such as glaciology, dendrochronology, sedimentology and hydrology. Based on different proxies, each research community produces different kinds of climatic or hydrologic reanalyses at different spatio-temporal scales and resolutions. When considering climate or hydrology, many studies have been devoted to characterising variability, trends or breaks using observed time series representing different regions or climates of the world. However, in hydrology, these studies have usually been limited to short temporal scales (mainly a few decades and more rarely a century) because they require observed time series (which suffer from a limited spatio-temporal density). This paper introduces ANATEM, a method that combines local observations and large-scale climatic information (such as the 20CR Reanalysis) to build long-term probabilistic air temperature and precipitation time series with a high spatio-temporal resolution (1 day and a few km2). ANATEM was tested on the reconstruction of air temperature and precipitation time series of 22 watersheds situated in the Durance River basin, in the French Alps. Based on a multi-criteria and multi-scale diagnosis, the results show that ANATEM improves the performance of classical statistical models – especially concerning spatial homogeneity – while providing an original representation of uncertainties which are conditioned by atmospheric circulation patterns. The ANATEM model has been also evaluated for the regional scale against independent long-term time series and was able to capture regional low-frequency variability over more than a century (1883–2010).

Study on the relationship between SAR-based digital elevation models and water vapor contents and surface deformation

2021 ◽  
Author(s):  
Yen-Yi Wu ◽  
Hsuan Ren
Keyword(s):  
Water Vapor ◽  
Prediction Models ◽  
Surface Deformation ◽  
Weather Prediction ◽  
Linear Regression Analysis ◽  
Sar Interferometry ◽  
Phase Gradient ◽  
Area Of Interest ◽  
Digital Elevation ◽  
The Relationship

<p>Synthetic Aperture Radar (SAR) Interferometry (InSAR) is a powerful tool in radar remote sensing. However, due to the unavoidable inherent limits of the SAR mechanism, there are different challenges to be tackled based on the user’s aim of use. Common issues that have been discussed in the application of topography mapping are temporal decorrelation, surface deformation, atmospheric disturbance, and phase unwrapping problems. These difficulties expose the quality of the final DEM products under high risks, depending on the selection of InSAR image pairs and the environment of area of interest. In this research, we are aiming at investigating the relationship between the SAR-based digital elevation model (DEM) and the related factors which contribute to the error budget. This research will allow InSAR technique users to obtain a better understanding of the severity of errors that were induced by the factors. Furthermore, by knowing which factor degrades the InSAR-generated DEMs the most, one could accordingly apply appropriate methods to reduce the error.</p><p>In this research, eight pairs of Sentinel-1A images are used. They are characterized by a 12-day temporal baseline and over 90 meters of perpendicular baseline. The conventional InSAR processing workflow is conducted in each pair. In the post-processing stage, phase gradient removal is applied in order to mitigate unwrapping problems. Surface deformation and water vapor variation are chosen as two factors that introduce errors in InSAR DEM. GPS data are collected to obtain deformation information, and atmospheric water vapor data are collected by weather prediction models. Finally, the multiple linear regression analysis is applied in order to find out the relationship between SAR-based DEM and the selected factors.</p>

scholarly journals Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

2019 ◽  
Vol 11 (2) ◽  
pp. 133 ◽  
Author(s):  
Meng Zhang ◽  
Hui Lin ◽  
Hua Sun ◽  
Yaotong Cai
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Temporal Resolution ◽  
Central China ◽  
Landsat 8 ◽  
Casa Model ◽  
Urbanized Area ◽  
Cloudy Weather ◽  
Urbanized Areas ◽  
Spatio Temporal

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.

Sign in / Sign up

Export Citation Format

Share Document