An Experimental Study of Spatial Variability of Rainfall

Tokay, Ali; Roche, Rosa M.; Bashor, Paul G.

doi:10.1175/jhm-d-13-031.1

Cited by 27 publications

(27 citation statements)

References 17 publications

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“…Even though the basins are very small, large spatial differences of rainfall exist due to localized heavy rainfall. These results are in general agreement with previous experimental studies for the spatial variability of rainfall [38][39][40]. …”

Section: Cross Validation Of Quantitative Precipitation Estimatessupporting

confidence: 93%

Effects of Using High-Density Rain Gauge Networks and Weather Radar Data on Urban Hydrological Analyses

Yoon

Lee

2017

Water

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Flood prediction is difficult in urban areas because only sparse gauge data and radar data of low accuracy are usually used to analyze flooding and inundation. Sub-basins of urban areas are extremely small, so rainfall data of high spatial resolution are required for analyzing complex drainage systems with high spatial variability. This study aimed to produce three types of quantitative precipitation estimation (QPE) products using rainfall data that was derived from 190 gauges, including the new high-density rain-gauge network operated by the SK Planet company, and the automated weather stations of the Korea Meteorological Administration, along with weather radar data. This study also simulated urban runoff for the Gangnam District of Seoul, South Korea, using the obtained QPE products to evaluate hydraulic and hydrologic impacts according to three rainfall fields. The accuracy of this approach was assessed in terms of the amount and spatial distribution of rainfall in an urban area. The QPE products provided highly accurate results and simulations of peak runoff and overflow phenomena. They also accurately described the spatial variability of the rainfall fields. Overall, the integration of high-density gauge data with radar data proved beneficial for quantitative rainfall estimation.

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Section: Cross Validation Of Quantitative Precipitation Estimatessupporting

confidence: 93%

Effects of Using High-Density Rain Gauge Networks and Weather Radar Data on Urban Hydrological Analyses

Yoon

Lee

2017

Water

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“…These differences may be partially related to the enhanced spatial variability of unorganized convective activity that is more dominant over warmer months (Tokay et al 2014). This is particularly true for coastal areas (Kingston) or in the Southeast, where daytime convection 712 could trigger afternoon pop-up thundershowers.…”

Section: B Precipitationmentioning

confidence: 99%

Observational Perspectives from U.S. Climate Reference Network (USCRN) and Cooperative Observer Program (COOP) Network: Temperature and Precipitation Comparison

Leeper

Rennie

Palecki

2015

Journal of Atmospheric and Oceanic Technology

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The U.S. Cooperative Observer Program (COOP) network was formed in the early 1890s to provide daily observations of temperature and precipitation. However, manual observations from naturally aspirated temperature sensors and unshielded precipitation gauges often led to uncertainties in atmospheric measurements. Advancements in observational technology (ventilated temperature sensors, well-shielded precipitation gauges) and measurement techniques (automation and redundant sensors), which improve observation quality, were adopted by NOAA's National Climatic Data Center (NCDC) into the establishment of the U.S. Climate Reference Network (USCRN). USCRN was designed to provide high-quality and continuous observations to monitor long-term temperature and precipitation trends, and to provide an independent reference to compare to other networks. The purpose of this study is to evaluate how diverse technological and operational choices between the USCRN and COOP programs impact temperature and precipitation observations. Naturally aspirated COOP sensors generally had warmer (10.488C) daily maximum and cooler (20.368C) minimum temperatures than USCRN, with considerable variability among stations. For precipitation, COOP reported slightly more precipitation overall (1.5%) with network differences varying seasonally. COOP gauges were sensitive to wind biases (no shielding), which are enhanced over winter when COOP observed (10.7%) less precipitation than USCRN. Conversely, wetting factor and gauge evaporation, which dominate in summer, were sources of bias for USCRN, leading to wetter COOP observations over warmer months. Inconsistencies in COOP observations (e.g., multiday observations, time shifts, recording errors) complicated network comparisons and led to unique bias profiles that evolved over time with changes in instrumentation and primary observer.

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“…The complexity in small-scale variability of rainfall increases in hilly terrain (Zängl, 2007;Li et al, 2014). The rainfall often becomes inhomogeneous due to topographic influence and is at times highly localized, resulting in large errors in the retrieved precipitation by passive/active remote sensors due to non-uniform beam-filling of precipitation within the satellite or radar pixel (Tokay and Ozturk, 2012). In order to understand the physical processes responsible for such variability, several studies examined the dependency of rainfall spatial variability (in terms of correlation distance, d o ) on rainfall regimes (Krajewski et al, 2003), seasons, spatial and temporal aggregation of data (Krajewski et al, 2003;Villarini et al, 2008;Luini and Capsoni, 2012;Chen et al, 2015;Prat and Nelson, 2015), sample size and extreme rain events (Habib et al, 2001) and geographical features like topography (Li et al, 2014).…”

mentioning

confidence: 99%

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

Sunilkumar

Rao

Satheeshkumar

2016

Hydrol. Earth Syst. Sci.

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Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and subdaily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of 6-12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1/4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70 % of total variance in the SWM and only ∼ 30 % in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both monsoon seasons. The 1 h resolution data show the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ∼ 30 km in both monsoon seasons.Validation of high-resolution rainfall estimates from various MPEs against the gauge rainfall data indicates that all MPEs underestimate the light and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions; however, considerable improvement is observed at 24 h resolution. Among the different MPEs investigated, the Climate Prediction Centre morphing technique (CMORPH) performs better at 3-hourly resolution in both monsoons. The performance of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) is much better at daily resolution than at 3-hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of the diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insign...

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An Experimental Study of Spatial Variability of Rainfall

Cited by 27 publications

References 17 publications

Effects of Using High-Density Rain Gauge Networks and Weather Radar Data on Urban Hydrological Analyses

Effects of Using High-Density Rain Gauge Networks and Weather Radar Data on Urban Hydrological Analyses

Observational Perspectives from U.S. Climate Reference Network (USCRN) and Cooperative Observer Program (COOP) Network: Temperature and Precipitation Comparison

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

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