Search Results (8)

It has been reported that systematic bias exists in gauge measurements, which are usually used as the evaluation benchmark, so it is crucial to investigate the impacts of gauge data bias on the evaluation of satellite precipitation products. Six satellite precipitation products (IMERG, CMORPH, GSMaP, PERSIANN, PERSIANN−CCS, and PDIR−Now) and gauge data are collected from 2003 to 2015 in the arid region of Northwestern China. A daily correction for precipitation biases from wind-induced undercatch, wetting loss, and trace error is made for gauge measurements. The changes in metrics, including four continuous and four categorical metrics, are calculated to illustrate how the gauge data bias impacts the evaluation of six satellite precipitation products. The results show the following: The overall performances of six satellite precipitation products are undervalued by the gauge bias. Compared to other satellite products, the performance of IMERG is the best, whether before or after bias correction. However, the performances of all six satellite products are still not good enough even after bias correction and need to be improved. The impacts of gauge bias on the evaluation of the satellite precipitation products also differ by subregion, season, satellite precipitation product, precipitation intensity, and precipitation phase. In conclusion, the impacts of the gauge bias on the performance assessment of satellite products are obvious over the study region, implying that bias correction for gauge measurements is needed to obtain an accurate understanding of the performance of satellite precipitation products if choosing the gauge data as the evaluation benchmark. Full article

This paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These data are prone to sensor noise and environment-induced measurement errors requiring an ordered set of quality control (QC) corrections using nearby weather station data. Sensor noise at valley sites with single-vibrating wire gauges accounted for the removal of 5% to 8% (49–76 mm) of annual precipitation. This was compensated for by an increase of 6% to 8% (50–76 mm) from under-catch. A three-wire ridge gauge did not experience significant sensor noise; however, the under-catch of snow resulted in 42% to 52% (784–1342 mm) increased precipitation. When all QC corrections were applied, the annual cumulative precipitation at the ridge demonstrated increases of 39% to 49% (731–1269 mm), while the valley gauge adjustments were −4% to 1% (−39 mm to 13 mm). Public sector totalizing precipitation gauge records often undergo minimal QC. Care must be exercised to check the corrections applied to such records when used to estimate watershed water balance or precipitation orographic enhancement. Systematic errors at open high-elevation sites may exceed nearby valley or forest sites. Full article

Tipping bucket rain gauges (TBR) are widely used worldwide because they are simple, cheap, and have low-energy consumption. However, their main disadvantage lies in measurement errors, such as those caused by rainfall intensity (RI) variation, which results in data underestimation, especially during extreme rainfall events. This work aims to understand these types of errors, identifying some of their causes through an analysis of water behavior and its effect on the TBR mechanism when RI increases. The mechanical biases of TBR effects on data were studied using 13 years of data measured at 10 TBRs in a mountain basin, and two semi-analytical approaches based on the TBR mechanism response to RI have been proposed, validated in the laboratory, and contrasted with a simple linear regression dynamic calibration and a static calibration through a root-mean-square error analysis in two different TBR models. Two main sources of underestimation were identified: one due to the cumulative surplus during the tipping movement and the other due to the surplus water contributed by the critical drop. Moreover, a random variation, not related to RI, was also observed, and three regions in the calibration curve were identified. Proposed calibration methods have proved to be an efficient alternative for TBR calibration, reducing data error by more than 50% in contrast with traditional static calibration. Full article

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions. Full article

Numerical studies of the wind-induced bias of precipitation measurements assume that turbulence is generated by the interaction of the airflow with the gauge body, while steady and uniform free-stream conditions are imposed. However, wind is turbulent in nature due to the roughness of the site and the presence of obstacles, therefore precipitation gauges are immersed in a turbulent flow. Further to the turbulence generated by the flow-gauge interaction, we investigated the natural free-stream turbulence and its influence on precipitation measurement biases. Realistic turbulence intensity values at the gauge collector height were derived from 3D sonic anemometer measurements. Large Eddy Simulations of the turbulent flow around a chimney-shaped gauge were performed under uniform and turbulent free-stream conditions, using geometrical obstacles upstream of the gauge to provide the desired turbulence intensity. Catch ratios for dry snow particles were obtained using a Lagrangian particle tracking model, and the collection efficiency was calculated based on a suitable particle size distribution. The collection efficiency in turbulent conditions showed stronger undercatch at the investigated wind velocity and snowfall intensity below 10 mm h⁻¹, demonstrating that adjustment curves based on the simplifying assumption of uniform free-stream conditions do not accurately portray the wind-induced bias of snow measurements. Full article

Gauges modify wind fields, producing important systematic errors (undercatching) in the measurement of solid precipitation (Ps), especially under windy conditions. A methodology that combines geostatistical techniques and hydrological models to perform a preliminary assessment of global undercatch and precipitation patterns in alpine regions is proposed. An assessment of temperature and precipitation fields is performed by applying geostatistical approaches assuming different hypothesis about the relationship between climatic fields and altitude. Several experiments using different approximations of climatic fields in different approaches to a hydrological model are evaluated. A new hydrological model, the Snow-Témez Model (STM), is developed including two parameters to correct the solid (Cs) and liquid precipitation (Cr). The procedure allows identifying the best combination of geostatistical approach and hydrological model for estimating streamflow in the Canales Basin, an alpine catchment of the Sierra Nevada (Spain). The sensitivity of the results to the correction of the precipitation fields is analyzed, revealing that the results of the streamflow simulation are improved when the precipitation is corrected considerably. High values of solid Cs are obtained, while Cr values, although smaller than the solid one, are also significant. Full article

Accurate spatial and temporal representation of precipitation is of utmost importance for hydrological applications. Uncertainties in available data sets increase with spatial resolution due to small-scale processes over complex terrain. As previous studies revealed high regional differences in the performance of gridded precipitation data sets, it is important to assess the related uncertainties at the catchment scale, where these data sets are typically applied, e.g., for hydrological modeling. In this study, the uncertainty of eight gridded precipitation data sets from various sources is investigated over an alpine catchment. A high resolution reference data set is constructed from station data and applied to quantify the contribution of spatial resolution to the overall uncertainty. While the results demonstrate that the data sets reasonably capture inter-annual variability, they show large seasonal differences. These increase for daily indicators assessing dry and wet spells as well as heavy precipitation. Although the higher resolution data sets, independent of their source, show a better agreement, the coarser data sets showed great potential especially in the representation of the overall climatology. To bridge the gaps in data scarce areas and to overcome the issues with observational data sets (e.g., undercatch and station density) it is important to include a variety of data sets and select an ensemble for a robust representation of catchment precipitation. However, the study highlights the importance of a thorough assessment and a careful selection of the data sets, which should be tailored to the desired application. Full article

The 2015 release of the precipitation climatology from the Global Precipitation Climatology Centre (GPCC) for 1951–2000, based on climatological normals of about 75,100 rain gauges, allows for quantification of mean land surface precipitation as part of the global water cycle. In GPCC’s 2011-release, a bulk climatological correction was applied to compensate for gauge undercatch. In this paper we derive an improved correction approach based on the synoptic weather reports for the period 1982–2015. The compared results show that the climatological approach tends to overestimate the correction for Central and Eastern Europe, especially in the northern winter, and in other regions throughout the year. Applying the mean weather-dependent correction to the GPCC’s uncorrected precipitation climatology for 1951–2000 gives a value of 854.7 mm of precipitation per year (excluding Antarctica) or 790 mm for the global land surface. The warming of nearly 1 K relative to pre-industrial temperatures is expected to be accompanied by a 2%–3% increase in global (land and ocean) precipitation. However, a comparison of climatology for 30-year reference periods from 1931–1960 up to 1981–2010 reveals no significant trend for land surface precipitation. This may be caused by the large variability of precipitation, the varying data coverage over time and other issues related to the sampling of rain-gauge networks. The GPCC continues to enlarge and further improve the quality of its database, and will generate precipitation analyses with homogeneous data coverage over time. Another way to reduce the sampling issues is the combination of rain gauge-based analyses with remote sensing (i.e., satellite or radar) datasets. Full article