scholarly journals Generating Multiyear Gridded Daily Rainfall over New Zealand

2005 ◽  
Vol 44 (9) ◽  
pp. 1315-1323 ◽  
Author(s):  
Andrew Tait ◽  
Richard Turner
Keyword(s):  
New Zealand ◽  
Spatial Interpolation ◽  
Mesoscale Model ◽  
Interpolation Method ◽  
Daily Rainfall ◽  
High Elevation ◽  
Flow Through ◽  
Spatial Interpolation Method ◽  
River Catchments

Abstract Daily rainfall totals are a key input for hydrological models that are designed to simulate water and pollutant flow through both soil and waterways. Within New Zealand there are large areas and many river catchments where no long-term rainfall observations exist. A method for estimating daily rainfall over the whole of New Zealand on a 5-km grid is described and tested over a period from January 1985 to April 2002. Improvement over a spatial interpolation method was gained by scaling high-elevation rainfall estimates using simulated mesoscale model rainfall surfaces that are generated for short periods in 1994 and 1996. This method is judged to produce reasonable and useful estimates of daily rainfall.

Spatial Simulation of Precipitation Resources in Chongqing

2012 ◽  
Vol 518-523 ◽  
pp. 4261-4265
Author(s):  
Xiao Song Lin ◽  
Sha Sha Yu ◽  
Hai Yan Wang
Keyword(s):  
Spatial Distribution ◽  
Spatial Interpolation ◽  
Regional Difference ◽  
Interpolation Method ◽  
Annual Precipitation ◽  
Regression Equations ◽  
Spatial Simulation ◽  
Simulation Data ◽  
Spatial Interpolation Method ◽  
Precise Simulation

Years’ precipitation data of Chongqing from 101 metrological stations has been adopted in the paper and the regression equations between annual precipitation and altitude, longitude, and height have been obtained by the use of SPSS, then elaborate simulation of Chongqing’s precipitation resources based on regression analysis was completed through the 1km×1km grid system and fitted equation. Elaborated simulation of precipitation resources was realized by best spatial interpolation method with the support of GIS; then the results of two different simulation methods were coupled in the form of linear combination to obtain the coupling simulation of spatial distribution of Chongqing’s precipitation resources, finally the precipitation resources were summed up and distributed according to different administration areas at county level and thus obtain precise simulation data of precipitation resources in each county of Chongqing. The results showed that there is a remarkable regional difference in the spatial distribution of precipitation resources of Chongqing, and it decreases from the southeast to the northwest in general, with the annual precipitation higher than 1270mm in southeast and lower than 1080mm in northwest.

scholarly journals Simulation of past variability in seasonal snow in the Southern Alps, New Zealand

1995 ◽  
Vol 21 ◽  
pp. 377-382 ◽  
Author(s):  
B.B. Fitzharris ◽  
C.E. Garr
Keyword(s):  
New Zealand ◽  
Southern Alps ◽  
Model Output ◽  
Total Water ◽  
Seasonal Snow ◽  
Temperature And Precipitation ◽  
Major River ◽  
River Catchments ◽  
Snow Deposition

There are no systematic measurements of seasonal snow in the Southern Alps, New Zealand, so little information is available as to its past variability. To rectify this, a conceptual model is developed that calculates seasonal snow deposition, ablation and accumulation. The model is based on daily temperature and precipitation data from long-established climate stations about the Southern Alps. Output is given as daily specific net balance of snow at five elevation bands from 1000 to 2200 m and as total water stored as seasonal snow over several major river catchments. Model output is in general agreement when tested against the few historical observations of snow and is tuned to the long-term water balance. A chronology of seasonal snow is reconstructed from 1931 to 1993. Area-averaged annual maxima average 366 mm. They show no trend, but large inter-annual variability from less than 200 to over 650 mm w.e. Seasonal snow can peak at any time between September and January.

Spectral and spatial interpolation of precipitation for daily and hourly time series

2020 ◽  
Author(s):  
Masoud Mehrvand ◽  
András Bárdossy
Keyword(s):  
Time Series ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Statistical Characteristics ◽  
Precipitation Time Series ◽  
Spectral Interpolation ◽  
Hourly Data ◽  
Spatial Interpolation Method ◽  
Synthetic Precipitation ◽  
Rainy Days

<p>Generating synthetic precipitation for weather generators were always a challenging issue in hydro-climate simulations because of its high variability in time and space. We present a spectral method for generating the synthetic precipitation time series which is in accordance with the observed precipitation statistical characteristics not only for the observed points, but also for any desired location by interpolating the time series spectrum. In this regard, time series spectra derived from the observed signal converting from its time domain to the corresponding frequency domain using the Fourier transform.</p><p>The main problem for spectral interpolation of precipitation time series is highly occurrence of non-rainy days which can be even more inaccurate for the finer resolutions such as hourly and sub-hourly data. In order to overcome the highly frequent occurrence of non-rainy days, transformation between indicator and normal correlation has been taken into account.</p><p>This method enables us to generate synthetic time series with same statistical characteristics for the observed points and also for any point of interests rather than the observed points. The introduced so called spectral and spatial interpolation method applied for daily and hourly precipitation time series for the selected stations in state Baden-Württemberg, Germany.</p>

scholarly journals Conditional spatial interpolation method for detecting minimally polluted areas with selective aerosol emissions to the city atmosphere

2020 ◽  
Vol 30 (3) ◽  
pp. 57-66
Author(s):  
H. H. Asadov ◽  
R. Sh. Mammadli
Keyword(s):  
Spatial Interpolation ◽  
Interpolation Method ◽  
Sampling Point ◽  
Base Points ◽  
Continuous Surface ◽  
Spatial Interpolation Method ◽  
Aerosol Emissions ◽  
The Impact ◽  
The City ◽  
Aerosol Source

Continuous surface interpolation is an important aspect of spatial analysis. A number of methods are used to interpolate a continuous surface, one of which is the spatial interpolation method with the Inverse Distance Weight (IDW) ratio. The purpose of this article is to develop a method of conditional spatial interpolation for finding such spatial points in the urban zone where the impact of selective accidental aerosol emissions into the city atmosphere is minimal. Conditional spatial interpolation refers to the case when the distances to the interpolated points are set by a certain condition, and it is necessary to determine the interpolated point where the above influence is minimal. In this case, spatial samples or base points used for interpolation are formed when a single powerful aerosol source is exposed to individual channels (distances). It is shown that there is an optimal relationship between the distances from the sampling point to the interpolation point and from the sampling point to the powerful aerosol source, at which the total effect of the powerful source on the interpolated point is minimal.

scholarly journals AN IMPROVED TEMPERATURE SPATIAL INTERPOLATION METHOD FOR SPACEBORNE LIDAR ATMOSPHERIC CORRECTION

2021 ◽  
Vol V-4-2021 ◽  
pp. 83-88
Author(s):  
M. Zhou ◽  
K. Li ◽  
M. Pan ◽  
J. Chen ◽  
C. Li ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Interpolation Method ◽  
Meteorological Parameter ◽  
Atmospheric Correction ◽  
Absolute Error ◽  
Lapse Rate ◽  
Interpolation Methods ◽  
Spatial Interpolation Method ◽  
Meteorological Elements ◽  
Inverse Distance

Abstract. As one of the most important meteorological elements, temperature is an indispensable meteorological parameter for the atmospheric correction of spaceborne LiDAR ranging. Given a limited number of surface meteorological observation stations, the temperature values for all region of LiDAR observation need to be interpolated using appropriate spatial interpolation methods. In this paper, based on the monthly surface observation values in individual years (1981–2010) of Sichuan province observation stations, we firstly analyze the effects of three common interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). To solve the problem of low interpolation accuracy in severely undulating terrain area, an improved gradient distance inverse square method based on the adiabatic lapse rate (GIDS-ALR) is proposed. The experimental results show that the GIDS-ALR has an obvious improvement in the effect of severely undulating terrain, where the absolute error has been improved by more than 43% in average. Additionally, the temperature-interpolated MAE is reduced by more than 30%. The effectiveness and applicability of the proposed method is verified in this paper.

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