Evaluate before use – temporal performance differences of gridded precipitation products in complex terrain

2020 ◽  
Author(s):  
Harald Zandler ◽  
Isabell Haag ◽  
Cyrus Samimi
Keyword(s):  
Monthly Precipitation ◽  
Observation Data ◽  
Full Data ◽  
Fourfold Increase ◽  
Surface Precipitation ◽  
Poor Region ◽  
Pamir Mountains ◽  
Evaluation Measure ◽  
Evaluation Approaches ◽  
The Impact

<p>Gridded precipitation data is of central importance for various geoscientific research applications and is often the only available resource to derive spatial and temporal rainfall quantities. Numerous studies exist that evaluate respective products using gauge measurements. However, many existing approaches ignore the impact of temporal changes in incorporated observation data, the location of the observations and the potential overlap of evaluation and dataset stations. Considering these issues, we quantitatively evaluated monthly precipitation values of frequently used precipitation raster datasets (GPCC Full Data Monthly Product Version 2018, GPCC Monitoring Product Version 6, CRU TS 4.03, GPCP Version 2.3, PERSIANN-CDR, TRMM 3B43, MERRA-2, MERRA-2 bias corrected, ERA5) in the peripheral Pamir mountains with a focus on the two periods 1980–1994 and 1998–2012 as they are characterized by considerable observation data changes. The coefficient of efficiency, a dimensionless hydroclimatic evaluation measure, showed that only three of the precipitation raster datasets (GPCC Full Data Monthly Product Version 2018, GPCC Monitoring Product Version 6, MERRA-2 bias corrected) are able to provide better surface precipitation values than the long-term station mean in this observation data poor region. Results of the gauge-based products also document a fourfold increase of errors during periods with low availability of station data compared to periods with higher observation data inputs. In conclusion, the study clearly illustrates that gridded precipitation products may be connected to major problems in peripheral mountain regions with limited measurement infrastructure as most datasets directly or indirectly depend on observation networks. Significant differences of errors related to incorporated observation data variations demonstrate the need for temporal and spatial evaluation approaches as a prerequisite for the scientific utilization of precipitation raster datasets.</p>

scholarly journals Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Harald Zandler ◽  
Isabell Haag ◽  
Cyrus Samimi
Keyword(s):  
Spatial Variations ◽  
Monthly Precipitation ◽  
Observation Data ◽  
Full Data ◽  
Surface Precipitation ◽  
Practical Applications ◽  
Temporal And Spatial Variations ◽  
Pamir Mountains ◽  
Significant Performance ◽  
Temporal And Spatial

Abstract Gridded datasets are of paramount importance to globally derive precipitation quantities for a multitude of scientific and practical applications. However, as most studies do not consider the impacts of temporal and spatial variations of included measurements in the utilized datasets, we conducted a quantitative assessment of the ability of several state of the art gridded precipitation products (CRU, GPCC Full Data Product, GPCC Monitoring Product, ERA-interim, ERA5, MERRA-2, MERRA-2 bias corrected, PERSIANN-CDR) to reproduce monthly precipitation values at climate stations in the Pamir mountains during two 15 year periods (1980–1994, 1998–2012) that are characterized by considerable differences in incorporated observation data. Results regarding the GPCC products illustrated a substantial and significant performance decrease with up to four times higher errors during periods with low observation inputs (1998–2012 with 2 stations on average per 124,000 km2) compared to periods with high quantities of regionally incorporated station data (1980–1994 with 14 stations on average per 124,000 km2). If independent stations were considered, the coefficient of efficiency indicated that only three of the gridded datasets (MERRA–2 bias corrected, GPCC, GPCC MP) performed better than the long term station mean for characterizing surface precipitation. Error patterns and magnitudes show that in complex terrain, evaluation of temporal and spatial variations of included observations is a prerequisite for using gridded precipitation products for scientific applications and to avoid overly optimistic performance assessments.

scholarly journals Study on applicability of remote sensing precipitation products in hilly-plain-wetland complex area of north China

2021 ◽  
Author(s):  
Zhu-Xian Wang ◽  
Zi-Yang Wang ◽  
Peng Feng ◽  
Yang Dong ◽  
Zhao-Wei Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Scale ◽  
Precipitation Intensity ◽  
Detection Accuracy ◽  
Monthly Precipitation ◽  
Observation Data ◽  
Hydrological Process ◽  
Surface Precipitation ◽  
True Value ◽  
Wetland Complex

Abstract For the hilly-plain-wetland complex ecosystem in the cold region of Northeast China, in order to solve the problems which include the scarcity of surface rainfall stations and the inability to provide accurate surface precipitation for hydrological process simulation, based on the observed precipitation of rainfall stations, three remote sensing precipitation products are taken as objects of evaluation. They include TRMM(Tropical Rainfall Measuring Mission) 3B42V7,3B42RT and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data,CHIP). In this paper, the observation data of rainfall stations and IDWP precipitation data interpolated by IDW(Inverse Distance Weighted) are used as true value of precision comparison, and the detection accuracy of remote sensing precipitation products from 2001 to 2010 is evaluated on the time scale (day, month and quarter) and spatial scale in Naoli River Basin.The results of the study indicated that 3B42V7 and CHIP have a high detection accuracy for precipitation, and their CC(correlation coefficient) values are 0.47 and 0.49 respectively in daily time scale. The accuracy of their observationfor monthly precipitation is better than that of daily precipitation, and the CC are 0.85 and 0.87 respectively. The multi-year average precipitation at different grid positions in the basin is overestimated by 3B42RT, and its evaluation results are poor at different time scales.For the precipitation intensity range of (0,20], the observed results of 3B42V7 and rainfall station are close to each other. For the precipitation intensity ranges of (0,1) and (50,+∞), 3B42RT and CHIP have overestimated or underestimated the precipitation in different degrees. Based on the above analysis results, 3B42RT can be considered as data that can detect whether precipitation occurs on different spatial positions in the basin. 3B42V7 and CHIP can be applied to flood forecasting and non-point source pollution control in cold regions.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

scholarly journals Lively Social Space, Well-Being Activity, and Urban Design: Findings From a Low-Cost Community-Led Public Space Intervention

2016 ◽  
Vol 49 (6) ◽  
pp. 685-716 ◽  
Author(s):  
Jamie Anderson ◽  
Kai Ruggeri ◽  
Koen Steemers ◽  
Felicia Huppert
Keyword(s):  
Public Space ◽  
Urban Design ◽  
Design Research ◽  
Local Community ◽  
Social Space ◽  
Low Cost ◽  
Well Being ◽  
Observation Data ◽  
Before And After ◽  
The Impact

Empirical urban design research emphasizes the support in vitality of public space use. We examine the extent to which a public space intervention promoted liveliness and three key behaviors that enhance well-being (“connect,” “be active,” and “take notice”). The exploratory study combined directly observed behaviors with self-reported, before and after community-led physical improvements to a public space in central Manchester (the United Kingdom). Observation data ( n = 22,956) and surveys (subsample = 212) were collected over two 3-week periods. The intervention brought significant and substantial increases in liveliness of the space and well-being activities. None of these activities showed increases in a control space during the same periods. The findings demonstrate the feasibility of the research methods, and the impact of improved quality of outdoor neighborhood space on liveliness and well-being activities. The local community also played a key role in conceiving of and delivering an effective and affordable intervention. The findings have implications for researchers, policy makers, and communities alike.

PENGARUH PENGETAHUAN PERPAJAKAN TERHADAP KEPATUHAN WAJIB PAJAK KENDARAAN BERMOTOR DI KANTOR SAMSAT KOTA TOMOHON

2021 ◽  
pp. 292-301
Author(s):  
Stevly Tumanduk ◽  
Arie Kawulur ◽  
Aprili Bacilius
Keyword(s):  
Motor Vehicle ◽  
Regression Coefficient ◽  
Primary Data ◽  
Survey Method ◽  
Observation Data ◽  
Simple Regression Analysis ◽  
Variable Influence ◽  
The Impact ◽  
Linearity Test ◽  
Simple Regression

Abstrak Riset ini bermaksud demi mengetahui apakah pengaruh pengetahuan perpajakan terhadap kepatuhan wajib pajak di Kantor SAMSAT Kota Tomohon. Pada riset ini variabel kepatuhan wajib pajak menjadi variabel dependen. Sampel pada pengkajian ini sebanyak 100 wajib pajak serta diambil memakai rumus Slovin.             Metode yang dipergunakan dalam riset ini ialah metode survei dengan pendekatan kuantitatif. Pada riset ini data primer dikumpulkan dengan cara teknik pengumpulan data observasi, dokumentasi serta angket/kuesioner, adapun teknik analisis data yang digunakan pada pengkajian ini ialah analisis regresi sederhana dengan uji normalitas, uji linieritas, dan uji hipotesis.             Hasil pengkajian ini adanya dampak positif dan signifikan mengenai dampak pengetahuan perpajakan terhadap kepatuhan wajib pajak kendaraan bermotor di mana persamaan regresi sederhana dalam riset ini menunjukkan nilai koefisien Regresi X sebesar 0.072 memperlihatkan ternyata setiap penambahan 1% pengaruh pengetahuan perpajakan, maka nilai kepatuhan wajib pajak kendaraan bermotor bertambah sebesar 0.072. Koefisien regresi tersebut bernilai positif, sehingga dapat dikatakan bahwa dampak variabel X terhadap Y ialah positif. Dengan demikian variabel pengaruh pengetahuan perpajakan berdampak signifikan atas kepatuhan wajib pajak kendaraan bermotor di Kantor Samsat Kota Tomohon.   Kata Kunci : Pengetahuan Perpajakan, Kepatuhan Wajib Pajak   Abstract                This research intends to find out whether the influence of tax knowledge on taxpayer compliance at the Tomohon City SAMSAT Office. In this research, the taxpayer compliance variable becomes the dependent variable. The sample in this study was 100 taxpayers and was taken using the Slovin formula. The method used in this research is a survey method with a quantitative approach. In this research, primary data were collected by means of observation data collection techniques, documentation and questionnaires, while the data analysis techniques used in this study were simple regression analysis with normality test, linearity test, and hypothesis testing. The results of the study are that there is a positive and significant impact on the impact of tax knowledge on motor vehicle taxpayer compliance where the simple regression equation in this research shows the X regression coefficient value of 0.072 showing that all 1% additions affect tax knowledge, then the motor vehicle taxpayer compliance value increases by 0.072. The regression coefficient is positive, so it can be said that the impact of variable X on Y is positive. Thus the variable influence of tax knowledge has a significant impact on motor vehicle taxpayer compliance at the Tomohon City Samsat Office.   Keywords: Tax Knowledge, Taxpayer Compliance

scholarly journals Forecasts covering one month using a cut-cell model

2013 ◽  
Vol 6 (4) ◽  
pp. 875-882 ◽  
Author(s):  
J. Steppeler ◽  
S.-H. Park ◽  
A. Dobler
Keyword(s):  
Cell Model ◽  
Control Model ◽  
Precipitation Forecast ◽  
Target Area ◽  
Strong Impact ◽  
Monthly Precipitation ◽  
Small Areas ◽  
Cut Cell ◽  
Terrain Following ◽  
The Impact

Abstract. This paper investigates the impact and potential use of the cut-cell vertical discretisation for forecasts covering five days and climate simulations. A first indication of the usefulness of this new method is obtained by a set of five-day forecasts, covering January 1989 with six forecasts. The model area was chosen to include much of Asia, the Himalayas and Australia. The cut-cell model LMZ (Lokal Modell with z-coordinates) provides a much more accurate representation of mountains on model forecasts than the terrain-following coordinate used for comparison. Therefore we are in particular interested in potential forecast improvements in the target area downwind of the Himalayas, over southeastern China, Korea and Japan. The LMZ has previously been tested extensively for one-day forecasts on a European area. Following indications of a reduced temperature error for the short forecasts, this paper investigates the model error for five days in an area influenced by strong orography. The forecasts indicated a strong impact of the cut-cell discretisation on forecast quality. The cut-cell model is available only for an older (2003) version of the model LM (Lokal Modell). It was compared using a control model differing by the use of the terrain-following coordinate only. The cut-cell model improved the precipitation forecasts of this old control model everywhere by a large margin. An improved, more transferable version of the terrain-following model LM has been developed since then under the name CLM (Climate version of the Lokal Modell). The CLM has been used and tested in all climates, while the LM was used for small areas in higher latitudes. The precipitation forecasts of the cut-cell model were compared also to the CLM. As the cut-cell model LMZ did not incorporate the developments for CLM since 2003, the precipitation forecast of the CLM was not improved in all aspects. However, for the target area downstream of the Himalayas, the cut-cell model considerably improved the prediction of the monthly precipitation forecast even in comparison with the modern CLM version. The cut-cell discretisation seems to improve in particular the localisation of precipitation, while the improvements leading from LM to CLM had a positive effect mainly on amplitude.

scholarly journals The Effects of Assimilating Conventional and ATOVS Data on Forecasted Near-Surface Wind with WRF-3DVAR

2015 ◽  
Vol 143 (1) ◽  
pp. 153-164 ◽  
Author(s):  
Feimin Zhang ◽  
Yi Yang ◽  
Chenghai Wang
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Lower Atmosphere ◽  
Observation Data ◽  
Initial Field ◽  
Infrared Observation ◽  
Forecast Performance ◽  
Near Surface ◽  
The Impact ◽  
Positive Effect

Abstract In this paper, the Weather Research and Forecasting (WRF) Model with the three-dimensional variational data assimilation (WRF-3DVAR) system is used to investigate the impact on the near-surface wind forecast of assimilating both conventional data and Advanced Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (ATOVS) radiances compared with assimilating conventional data only. The results show that the quality of the initial field and the forecast performance of wind in the lower atmosphere are improved in both assimilation cases. Assimilation results capture the spatial distribution of the wind speed, and the observation data assimilation has a positive effect on near-surface wind forecasts. Although the impacts of assimilating ATOVS radiances on near-surface wind forecasts are limited, the fine structure of local weather systems illustrated by the WRF-3DVAR system suggests that assimilating ATOVS radiances has a positive effect on the near-surface wind forecast under conditions that ATOVS radiances in the initial condition are properly amplified. Assimilating conventional data is an effective approach for improving the forecast of the near-surface wind.

scholarly journals Managed infrastructure and services to empower meteorological services in developing countries

2021 ◽  
Author(s):  
Tom Smith
Keyword(s):  
Developing Countries ◽  
Life Cycle ◽  
Value Chain ◽  
Life Cycle Management ◽  
Observation Data ◽  
Negative Consequences ◽  
Observation Systems ◽  
Spatial Coverage ◽  
The World ◽  
The Impact

<p>Often in developing countries the spatial coverage with surface weather observations is sparse and the reliability of existing systems is lower than in other parts of the world. These gaps in the availability of observation data have significant negative consequences, locally and globally. For decades international funds have been used to acquire meteorological infrastructure with little to no focus on life-cycle management. Furthermore, improvements in one part of the value chain are often not connected with further downstream services meaning local benefits are generated with substantial delay, if at all.</p><p>DTN is one of the few organizations offering comprehensive solutions across the value chain from deployment and operation of observation systems through to weather analytics creating valuable insights for business, consumers and governments across the globe. DTN not only project manages the setup of weather observation systems but also maintains and operates measurement networks on different continents. The sensor agnostic approach enables us to offer the right sensor solution for each situation.</p><p>We see an opportunity to correct the mistakes of the past, changing the focus from acquiring observation systems to life cycle management to ensure the systems are maintained and leveraged effectively to provide forecasts and warnings for protection of life and property and enabling NMSs to focus on fulfilling their mission.</p><p>Funding organizations such as the World Bank must change the focus from hardware procurement to a performance-based PPE/P model that ensures the value of investments in infrastructure are realized. This sustainable approach will; ensure long lasting partnerships, harness the innovation in the private sector, create local jobs maintaining infrastructure and enable economic development through improved ability to manage the impact of weather and climate events.</p>

scholarly journals Deformation of rockfill in bodies of rockfill dams

2019 ◽  
pp. 5-5
Author(s):  
Mikhail Sainov
Keyword(s):  
Reinforced Concrete ◽  
Deformation Modulus ◽  
Deformation Model ◽  
Observation Data ◽  
Linear Deformation ◽  
Laboratory Test Results ◽  
Wide Range ◽  
Deformation Properties ◽  
Non Linear ◽  
The Impact

Introduction. The main factor determining the stress-strain state (SSS) of rockfill dam with reinforced concrete faces is deformability of the dam body material, mostly rockfill. However, the deformation properties of rockfill have not been sufficiently studied yet for the time being due to technical complexity of the matter, Materials and methods. To determine the deformation parameters of rockfill, scientific and technical information on the results of rockfill laboratory tests in stabilometers were collected and analyzed, as well as field data on deformations in the existing rockfill dams. After that, the values of rockfill linear deformation modulus obtained in the laboratory and in the field were compared. The laboratory test results were processed and analyzed to determine the parameters of the non-linear rockfill deformation model. Results. Analyses of the field observation data demonstrates that the deformation of the rockfill in the existing dams varies in a wide range: its linear deformation modulus may vary from 30 to 500 МPа. It was found out that the results of the most rockfill tests conducted in the laboratory, as a rule, approximately correspond to the lower limit of the rockfill deformation modulus variation range in the bodies of the existing dams. This can be explained by the discrepancy in density and particle sizes of model and natural soils. Only recently, results of rockfill experimental tests were obtained which were comparable with the results of the field measurements. They demonstrate that depending on the stress state the rockfill linear deformation modulus may reach 700 МPа. The processing of the results of those experiments made it possible to determine the parameters on the non-linear model describing the deformation of rockfill in the dam body. Conclusions. The obtained data allows for enhancement of the validity of rockfill dams SSS analyses, as well as for studying of the impact of the non-linear character of the rockfill deformation on the SSS of reinforced concrete faces of rockfill dams.

scholarly journals Uncertainty in runoff based on Global Climate Model precipitation and temperature data – Part 2: Estimation and uncertainty of annual runoff and reservoir yield

2014 ◽  
Vol 11 (5) ◽  
pp. 4579-4638 ◽  
Author(s):  
M. C. Peel ◽  
R. Srikanthan ◽  
T. A. McMahon ◽  
D. J. Karoly
Keyword(s):  
Climate Change ◽  
Standard Deviation ◽  
Climate Change Impact ◽  
Global Climate ◽  
Annual Runoff ◽  
Monthly Precipitation ◽  
Change Impact ◽  
The Impact ◽  
Reservoir Yield ◽  
Precipitation And Temperature

Abstract. Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between Global Climate Models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) datasets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to approximate within-GCM uncertainty of monthly precipitation and temperature projections and assess its impact on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. To-date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2014) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), temperature (MAT) and runoff (MAR), the standard deviation of annual precipitation (SDP) and runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 world-wide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainty from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were: MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma procedure was applied to each annual runoff time-series for hypothetical reservoir capacities of 1× MAR and 3× MAR and the average uncertainty in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were: 25.1% (1× MAR) and 11.9% (3× MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1× MAR or 3× MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

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