scholarly journals Research on the Influence of Small-Scale Terrain on Precipitation

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 805
Author(s):  
Wenya Gu ◽  
Xiaochen Zhu ◽  
Xiangrui Meng ◽  
Xinfa Qiu
Keyword(s):  
Large Scale ◽  
Central China ◽  
Distribution Model ◽  
Small Scale ◽  
Enhancement Effect ◽  
Monthly Precipitation ◽  
Layer By Layer ◽  
Precipitation Data ◽  
Precipitation Distribution ◽  
Different Levels

Terrain plays an important role in the formation, development and distribution of local precipitation and is a major factor leading to locally abnormal weather in weather systems. Although small-scale topography has little influence on the spatial distribution of precipitation, it interferes with precipitation fitting. Due to the arbitrary combination of small, medium and large-scale terrain, complex terrain distribution is formed, and small-scale terrain cannot be clearly defined and removed. Based on the idea of bidimensional empirical mode decomposition (BEMD), this paper extracts small-scale terrain data layer by layer to smooth the terrain and constructs a macroterrain model for different scales in Central China. Based on the precipitation distribution model using multiple regression, precipitation models (B0, B1, B2 and B3) of different scales are constructed. The 18-year monthly average precipitation data of each station are compared with the precipitation simulation results under different scales of terrain and TRMM precipitation data, and the influence of different levels of small-scale terrain on the precipitation distribution is analysed. The results show that (1) in Central China, the accuracy of model B2 is much higher than that of TRMM model A and monthly precipitation model B0. The comprehensive evaluation indexes are increased by 3.31% and 1.92%, respectively. (2) The influence of different levels of small-scale terrain on the precipitation distribution is different. The first- and second-order small-scale terrain has interference effects on precipitation fitting, and the third-order small-scale terrain has an enhancement effect on precipitation. However, the effect of small-scale topography on the precipitation distribution is generally reflected as interference.

scholarly journals DO THE VISUAL COMPLEXITY ALGORITHMS MATCH THE GENERALIZATION PROCESS IN GEOGRAPHICAL DISPLAYS?

2016 ◽  
Vol XLI-B2 ◽  
pp. 375-378 ◽  
Author(s):  
A. Brychtová ◽  
A. Çöltekin ◽  
V. Pászto
Keyword(s):  
Design Process ◽  
Large Scale ◽  
Satellite Images ◽  
Visual Complexity ◽  
Small Scale ◽  
Complexity Measures ◽  
Cartographic Generalization ◽  
Visual Clutter ◽  
Shaded Relief ◽  
Different Levels

In this study, we first develop a hypothesis that existing quantitative visual complexity measures will overall reflect the level of cartographic generalization, and test this hypothesis. Specifically, to test our hypothesis, we first selected common geovisualization types (i.e., cartographic maps, hybrid maps, satellite images and shaded relief maps) and retrieved examples as provided by Google Maps, OpenStreetMap and SchweizMobil by swisstopo. Selected geovisualizations vary in cartographic design choices, scene contents and different levels of generalization. Following this, we applied one of Rosenholtz et al.’s (2007) visual clutter algorithms to obtain quantitative visual complexity scores for screenshots of the selected maps. We hypothesized that visual complexity should be constant across generalization levels, however, the algorithm suggested that the complexity of small-scale displays (less detailed) is higher than those of large-scale (high detail). We also observed vast differences in visual complexity among maps providers, which we attribute to their varying approaches towards the cartographic design and generalization process. Our efforts will contribute towards creating recommendations as to how the visual complexity algorithms could be optimized for cartographic products, and eventually be utilized as a part of the cartographic design process to assess the visual complexity.

scholarly journals Precipitation Atlas for Germany (GePrA)

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 737
Author(s):  
Christopher Jung ◽  
Dirk Schindler
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Spline Interpolation ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Small Scale ◽  
Monthly Precipitation ◽  
New Approach ◽  
Very High Spatial Resolution

A new approach for modeling daily precipitation (RR) at very high spatial resolution (25 m × 25 m) was introduced. It was used to develop the Precipitation Atlas for Germany (GePrA). GePrA is based on 2357 RR time series measured in the period 1981–2018. It provides monthly percentiles (p) of the large-scale RR patterns which were mapped by a thin plate spline interpolation (TPS). A least-squares boosting (LSBoost) approach and orographic predictor variables (PV) were applied to integrate the small-scale precipitation variability in GePrA. Then, a Weibull distribution (Wei) was fitted to RRp. It was found that the mean monthly sum of RR ( R R ¯ s u m ) is highest in July (84 mm) and lowest in April (49 mm). A great dependency of RR on the elevation (ε) was found and quantified. Model validation at 425 stations showed a mean coefficient of determination (R2) of 0.80 and a mean absolute error (MAE) of less than 10 mm in all months. The high spatial resolution, including the effects of the local orography, make GePrA a valuable tool for various applications. Since GePrA does not only describe R R ¯ s u m , but also the entire monthly precipitation distributions, the results of this study enable the seasonal differentiation between dry and wet period at small scales.

Accuracy analysis of IMERG and CMORPH precipitation data over North China

2020 ◽  
Vol 81 ◽  
pp. 55-70
Author(s):  
L Shen ◽  
R Lin ◽  
L Lu ◽  
C Xu ◽  
Y Liu
Keyword(s):  
Large Scale ◽  
North China ◽  
Correlation Coefficients ◽  
Coastal Areas ◽  
Probability Of Detection ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Daily Precipitation Data ◽  
Mean Square Errors ◽  
Rainy Days

Large-scale agricultural production in North China makes the study of precipitation in this area vital. The performance of the Integrated Merged Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) and the Climate Prediction Center morphing technique (CMORPH) precipitation products for 2015 was evaluated against daily precipitation data from 404 rain gauges in North China. Relative errors, correlation coefficients, Pearson’s chi-squared test values, and root mean square errors, as well as the probability of detection (POD), false alarm ratio, and critical success index, were used to analyze the accuracy of both IMERG and CMORPH precipitation products on daily, monthly, and seasonal timescales. The probability density function (PDF) was also considered. Overall, both products overestimated ground precipitation, especially in summer. Positive correlation coefficients between satellite-derived and rain-gauge monthly precipitation data were higher over plains and coastal areas, compared with plateau regions. The PODs of both IMERG and CMORPH data were highest in summer. The PODs of IMERG data were much higher than for CMORPH data in autumn. The PODs over coastal regions, plains, and plateaus at lower latitudes also were considerably better than over inland and plateau areas at higher latitudes. The precipitation products performed best over coastal areas, plains, and areas with high rainfall. Both CMORPH and IMERG products were prone to identifying non-rainy days as rainy days. They also overestimated light (0.1-9.9 mm d-1) and moderate (10-24.9 mm d-1) precipitation events, although the IMERG product was more sensitive to precipitation. Accordingly, we find that both of these satellite-derived precipitation products require further modification to enable them to substitute for gauge precipitation data in North China.

scholarly journals Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition

2019 ◽  
Vol 11 (21) ◽  
pp. 2472
Author(s):  
He ◽  
Wang ◽  
Chang ◽  
Zhang ◽  
Feng
Keyword(s):  
Large Scale ◽  
Imaging Systems ◽  
Small Scale ◽  
Layer By Layer ◽  
Multiscale Decomposition ◽  
Cumulative Error ◽  
Scale Characteristics ◽  
The Difference ◽  
Color Cast ◽  
Error Suppression

Stripes are common in remote sensing imaging systems equipped with multichannel time delay integration charge-coupled devices (TDI CCDs) and have different scale characteristics depending on their causes. Large-scale stripes appearing between channels are difficult to process by most current methods. The framework of column-by-column nonuniformity correction (CCNUC) is introduced to eliminate large-scale stripes. However, the worst problem of CCNUC is the unavoidable cumulative error, which will cause an overall color cast. To eliminate large-scale stripes and suppress the cumulative error, we proposed a destriping method via unidirectional multiscale decomposition (DUMD). The striped image was decomposed by constructing a unidirectional pyramid and making difference maps layer by layer. The highest layer of the pyramid was processed by CCNUC to eliminate large-scale stripes, and multiple cumulative error suppression measures were performed to reduce overall color cast. The difference maps of the pyramid were processed by a designed filter to eliminate small-scale stripes. Experiments showed that DUMD had good destriping performance and was robust with respect to different terrains.

scholarly journals DO THE VISUAL COMPLEXITY ALGORITHMS MATCH THE GENERALIZATION PROCESS IN GEOGRAPHICAL DISPLAYS?

2016 ◽  
Vol XLI-B2 ◽  
pp. 375-378
Author(s):  
A. Brychtová ◽  
A. Çöltekin ◽  
V. Pászto
Keyword(s):  
Design Process ◽  
Large Scale ◽  
Satellite Images ◽  
Visual Complexity ◽  
Small Scale ◽  
Complexity Measures ◽  
Cartographic Generalization ◽  
Visual Clutter ◽  
Shaded Relief ◽  
Different Levels

In this study, we first develop a hypothesis that existing quantitative visual complexity measures will overall reflect the level of cartographic generalization, and test this hypothesis. Specifically, to test our hypothesis, we first selected common geovisualization types (i.e., cartographic maps, hybrid maps, satellite images and shaded relief maps) and retrieved examples as provided by Google Maps, OpenStreetMap and SchweizMobil by swisstopo. Selected geovisualizations vary in cartographic design choices, scene contents and different levels of generalization. Following this, we applied one of Rosenholtz et al.’s (2007) visual clutter algorithms to obtain quantitative visual complexity scores for screenshots of the selected maps. We hypothesized that visual complexity should be constant across generalization levels, however, the algorithm suggested that the complexity of small-scale displays (less detailed) is higher than those of large-scale (high detail). We also observed vast differences in visual complexity among maps providers, which we attribute to their varying approaches towards the cartographic design and generalization process. Our efforts will contribute towards creating recommendations as to how the visual complexity algorithms could be optimized for cartographic products, and eventually be utilized as a part of the cartographic design process to assess the visual complexity.

scholarly journals Evaluation of the ERS Scatterometer-Derived Soil Water Index to Monitor Water Availability and Precipitation Distribution at Three Different Scales in China

2008 ◽  
Vol 9 (3) ◽  
pp. 549-562 ◽  
Author(s):  
Deming Zhao ◽  
Claudia Kuenzer ◽  
Congbin Fu ◽  
Wolfgang Wagner
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Availability ◽  
Local Area ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Precipitation Distribution ◽  
Water Index ◽  
Global Precipitation Climatology Centre

Abstract In this paper, the capability of the European Remote Sensing Satellite (ERS) scatterometer-derived soil water index (SWI) data to disclose water availability and precipitation distribution in China is investigated. Monthly averaged SWI data for the years 1992–2000 are analyzed to evaluate the use of the SWI as an index to monitor water availability and water stress at three different scales in China and to investigate if it reflects general precipitation distribution characteristics in China. Monthly averaged in situ relative soil moisture from Chinese meteorological gauge stations, as well as monthly precipitation data from the Global Precipitation Climatology Centre (GPCC), are employed to perform comparisons with SWI on local, regional, and countrywide scales. First, since soil moisture is highly affected by the precipitation, area-averaged SWI is compared with in situ relative soil moisture and GPCC precipitation data in one local area. Second, area-averaged SWI and GPCC precipitation data are used to perform comparisons in three regions of China. Finally, the relationship between SWI and GPCC precipitation data in China is investigated on a countrywide scale. Such multiscale analyses with SWI data have not been performed before, and SWI has never been investigated in detail for China. ERS-derived SWI data in China for the years 1992–2000 are evaluated to be a good indicator for water availability on local, regional, and countrywide scales. SWI and SWI anomaly data correlate well with precipitation and in situ soil moisture data. SWI has furthermore been demonstrated to reflect extreme events such as droughts and floods in China, occurring during the investigated period between 1992 and 2000. Additionally, the SWI allows one to monitor increasing soil moisture resulting from snowmelt, which cannot be deduced from precipitation data. The freely available 15-yr (1992–2007) time series SWI data are thus a valuable tool to overcome the scarcity of in situ soil moisture observations, which are usually not available on regional and countrywide scales.

scholarly journals A Map Is a Living Structure with the Recurring Notion of Far More Smalls than Larges

2020 ◽  
Vol 9 (6) ◽  
pp. 388
Author(s):  
Bin Jiang ◽  
Terry Slocum
Keyword(s):  
Large Scale ◽  
Physical Phenomenon ◽  
Small Scale ◽  
Geographic Space ◽  
Continuous Map ◽  
Iterative System ◽  
Christopher Alexander ◽  
Topological Connectivity ◽  
Small Things ◽  
Different Levels

The Earth’s surface or any territory is a coherent whole or subwhole, in which the notion of “far more small things than large ones” recurs at different levels of scale ranging from the smallest of a couple of meters to the largest of the Earth’s surface or that of the territory. The coherent whole has the underlying character called wholeness or living structure, which is a physical phenomenon pervasively existing in our environment and can be defined mathematically under the new third view of space conceived and advocated by Christopher Alexander: space is neither lifeless nor neutral, but a living structure capable of being more alive or less alive. This paper argues that both the map and the territory are a living structure, and that it is the inherent hierarchy of “far more smalls than larges” that constitutes the foundation of maps and mapping. It is the underlying living structure of geographic space or geographic features that makes maps or mapping possible, i.e., larges to be retained, while smalls to be omitted in a recursive manner (Note: larges and smalls should be understood broadly and wisely, in terms of not only sizes, but also topological connectivity and semantic meaning). Thus, map making is largely an objective undertaking governed by the underlying living structure, and maps portray the truth of the living structure. Based on the notion of living structure, a map can be considered to be an iterative system, which means that the map is the map of the map of the map, and so on endlessly. The word endlessly means continuous map scales between two discrete ones, just as there are endless real numbers between 1 and 2. The iterated map system implies that each of the subsequent small-scale maps is a subset of the single large-scale map, not a simple subset but with various constraints to make all geographic features topologically correct.

Turbulence: the filtering approach

1992 ◽  
Vol 238 ◽  
pp. 325-336 ◽  
Author(s):  
M. Germano
Keyword(s):  
Large Eddy Simulation ◽  
Turbulent Flows ◽  
Large Scale ◽  
Stokes Equations ◽  
Small Scale ◽  
Mean Values ◽  
Eddy Simulation ◽  
Turbulent Field ◽  
Large Eddy ◽  
Different Levels

Explicit or implicit filtered representations of chaotic fields like spectral cut-offs or numerical discretizations are commonly used in the study of turbulence and particularly in the so-called large-eddy simulations. Peculiar to these representations is that they are produced by different filtering operators at different levels of resolution, and they can be hierarchically organized in terms of a characteristic parameter like a grid length or a spectral truncation mode. Unfortunately, in the case of a general implicit or explicit filtering operator the Reynolds rules of the mean are no longer valid, and the classical analysis of the turbulence in terms of mean values and fluctuations is not so simple.In this paper a new operatorial approach to the study of turbulence based on the general algebraic properties of the filtered representations of a turbulence field at different levels is presented. The main results of this analysis are the averaging invariance of the filtered Navier—Stokes equations in terms of the generalized central moments, and an algebraic identity that relates the turbulent stresses at different levels. The statistical approach uses the idea of a decomposition in mean values and fluctuations, and the original turbulent field is seen as the sum of different contributions. On the other hand this operatorial approach is based on the comparison of different representations of the turbulent field at different levels, and, in the opinion of the author, it is particularly fitted to study the similarity between the turbulence at different filtering levels. The best field of application of this approach is the numerical large-eddy simulation of turbulent flows where the large scale of the turbulent field is captured and the residual small scale is modelled. It is natural to define and to extract from the resolved field the resolved turbulence and to use the information that it contains to adapt the subgrid model to the real turbulent field. Following these ideas the application of this approach to the large-eddy simulation of the turbulent flow has been produced (Germano et al. 1991). It consists in a dynamic subgrid-scale eddy viscosity model that samples the resolved scale and uses this information to adjust locally the Smagorinsky constant to the local turbulence.

scholarly journals Predicting Bird Distributions Under Global Change

2021 ◽  
Author(s):  
Maggie MacPherson ◽  
Kevin R Burgio ◽  
Matthew DeSaix ◽  
Benjamin Freeman ◽  
John Herbert ◽  
...  
Keyword(s):  
Global Change ◽  
Large Scale ◽  
Species Distribution Model ◽  
Predictive Distribution ◽  
Distribution Model ◽  
Present Species ◽  
Small Scale ◽  
Explanatory Variables ◽  
Niche Concept ◽  
Analytical Approaches

Global change creates an urgent need to predict spatial responses of biota to support the conservation of sufficient habitat to maintain biodiversity. We present species distribution model theory and a synthesis of avian literature on approaches to collecting occurrence data, selecting explanatory variables and analytical processes currently in use to predict future distributions. We find that interpreting the validity of current predictive distributions is hindered by variation in spatio-temporal resolution of data sets that force hypothesis testing under the Grinnellian niche concept. Broadly, the capacity of species to shift their geographic ranges under land use and climate change is expected to be limited by both large scale (i.e., the physiological or fundamental niche) and small scale (i.e., the realized or tolerance niche) factors. We highlight the strengths and weaknesses of widely used explanatory variables and analytical approaches tailored to macrohabitat characteristics and the Grinnellian niche concept. This synthesis addresses if and how current approaches align with theory and makes recommendations for future directions to improve the accuracy of predictive distribution modelling.

scholarly journals Improvement of Spatial Interpolation of Precipitation Distribution Using Cokriging Incorporating Rain-Gauge and Satellite (SMOS) Soil Moisture Data

2021 ◽  
Vol 13 (5) ◽  
pp. 1039
Author(s):  
Bogusław Usowicz ◽  
Jerzy Lipiec ◽  
Mateusz Łukowski ◽  
Jan Słomiński
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Spatial Interpolation ◽  
Predictive Performance ◽  
Rain Gauge ◽  
Coefficient Of Determination ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Continuous Precipitation ◽  
Precipitation Distribution

Precipitation data provide a crucial input for examining hydrological issues, including watershed management and mitigation of the effects of floods, drought, and landslides. However, they are collected frequently from the scarce and often insufficient network of ground-based rain-gauge stations to generate continuous precipitation maps. Recently, precipitation maps derived from satellite data have not been sufficiently linked to ground-based rain gauges and satellite-derived soil moisture to improve the assessment of precipitation distribution using spatial statistics. Kriging methods are used to enhance the estimation of the spatial distribution of precipitations. The aim of this study was to assess two geostatistical methods, ordinary kriging (OK) and ordinary cokriging (OCK), and one deterministic method (i.e., inverse distance weighting (IDW)) for improved spatial interpolation of quarterly and monthly precipitations in Poland and near-border areas of the neighbouring countries (~325,000 or 800,000 km2). Quarterly precipitation data collected during a 5-year period (2010–2014) from 113–116 rain-gauge stations located in the study area were used. Additionally, monthly precipitations in the years 2014–2017 from over 400 rain-gauge stations located in Poland were used. The spatiotemporal data on soil moisture (SM) from the Soil Moisture and Ocean Salinity (SMOS) global satellite (launched in 2009) were used as an auxiliary variable in addition to precipitation for the OCK method. The predictive performance of the spatial distribution of precipitations was the best for OCK for all quarters, as indicated by the coefficient of determination (R2 = 0.944–0.992), and was less efficient (R2 = 0.039–0.634) for the OK and IDW methods. As for monthly precipitation, the performance of OCK was considerably higher than that of IDW and OK, similarly as with quarterly precipitation. The performance of all interpolation methods was better for monthly than for quarterly precipitations. The study indicates that SMOS data can be a valuable source of auxiliary data in the cokriging and/or other multivariate methods for better estimation of the spatial distribution of precipitations in various regions of the world.

Sign in / Sign up

Export Citation Format

Share Document