scholarly journals Evaluation of IMERG-E Precipitation Estimates for Fire Weather Applications in Alaska

2020 ◽  
Vol 35 (5) ◽  
pp. 1831-1843 ◽  
Author(s):  
Taylor A. Gowan ◽  
John D. Horel
Keyword(s):  
Situational Awareness ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Fire Weather ◽  
Spatial Coverage ◽  
Precipitation Estimates ◽  
Grid Points ◽  
Meteorological Observations ◽  
Forest Fire Danger

AbstractLarge wildfire outbreaks in Alaska are common from June to August. The Canadian Forest Fire Danger Rating System (CFFDRS) is used operationally by Alaskan fire managers to produce statewide fire weather outlooks and forecast guidance near active wildfires. The CFFDRS estimates of fire potential and behavior rely heavily on meteorological observations (precipitation, temperature, wind speed, and relative humidity) from the relatively small number of in situ stations across Alaska with precipitation being the most critical parameter. To improve the spatial coverage of precipitation estimates across Alaska for fire weather applications, a multisatellite precipitation algorithm was evaluated during six fire seasons (1 June–31 August 2014–19). Near-real-time daily precipitation estimates from the Integrated Multisatellite Retrievals for the Global Precipitation Mission (IMERG) algorithm were verified using 322 in situ stations across four Alaskan regions. For each region, empirical cumulative distributions of daily precipitation were obtained from station observations during each summer, and compared to corresponding distributions of interpolated values from IMERG grid points (0.1° × 0.1° grid). The cumulative distributions obtained from IMERG exhibited wet biases relative to the observed distributions for all regions, precipitation amount ranges, and summers. A bias correction approach using regional quantile mapping was developed to mitigate for the IMERG wet bias. The bias-adjusted IMERG daily precipitation estimates were then evaluated and found to produce improved gridded IMERG precipitation estimates. This approach may help to improve situational awareness of wildfire potential across Alaska and be appropriate for other high-latitude regions where there are sufficient in situ precipitation observations to help correct the IMERG precipitation estimates.

scholarly journals Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

2019 ◽  
Vol 11 (23) ◽  
pp. 2741 ◽  
Author(s):  
Aminyavari ◽  
Saghafian ◽  
Sharifi
Keyword(s):  
Weather Prediction ◽  
Precipitation Amount ◽  
Early Warning Systems ◽  
Flood Impacts ◽  
Spatial Coverage ◽  
Basin Scale ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Threshold ◽  
Precipitation Thresholds

Precipitation monitoring and early warning systems are required to reduce negative flood impacts. In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Moreover, with regard to mean precipitation at the basin scale, UKMO and European Center for Medium-Range Weather Forecasts (ECMWF) models in the Gorganrud Basin, ECMWF in the Karkheh Basin and UKMO in the Karun Basin performed better than others in flood forecasting. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the ECMWF had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models.

scholarly journals How Well Do Gridded Datasets of Observed Daily Precipitation Compare over Australia?

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Steefan Contractor ◽  
Lisa V. Alexander ◽  
Markus G. Donat ◽  
Nicholas Herold
Keyword(s):  
Daily Precipitation ◽  
Spatiotemporal Variability ◽  
Annual Maximum ◽  
Satellite Measurements ◽  
Temporal Correlations ◽  
Precipitation Estimates ◽  
In Situ Observations ◽  
Structural Uncertainties ◽  
Substantial Uncertainty

Daily gridded precipitation data are needed for investigating spatiotemporal variability of precipitation, including extremes; however, uncertainties related to daily precipitation products are large. Here, we compare a range of precipitation grids for Australia. These datasets include products derived solely from in situ observations (interpolated datasets) and two products that combine both remote sensed data and in situ observations. We find that all precipitation grids have similar climatologies for annual aggregated precipitation totals and annual maximum precipitation. The temporal correlations of daily precipitation values are higher between the interpolated datasets, but the correlations between the most widely used interpolated product (AWAP) and the two remotely sensed products (TRMM and GPCP) are still reasonable. Our results, however, point to distinct structural uncertainties between those datasets gridding in situ observations and those datasets deriving precipitation estimates primarily from satellite measurements. All datasets analysed agree well for low to moderate daily precipitation amounts up to about 20 mm but diverge at upper quantiles, indicating that substantial uncertainty exists in gridded precipitation extremes over Australia.

Performance evaluation of ensemble precipitation forecasts and satellite products for the spring 2019 severe floods in Iran

2021 ◽  
Author(s):  
Saleh Aminyavari ◽  
Bahram Saghafian ◽  
Ehsan Sharifi
Keyword(s):  
Spatial Distribution ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Warning System ◽  
Extreme Precipitation Events ◽  
Spatial Coverage ◽  
Precipitation Estimates ◽  
Precipitation Threshold ◽  
Precipitation Thresholds ◽  
Precipitation Events

<p>In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the TIGGE database as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG-RT V05B, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in two modes: spatial distribution of precipitation and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Although, generally, the models captured the spatial distribution of heavy precipitation events, the hot spots were not located in the correct area. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the Medium-Range Weather Forecasts (ECMWF) had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models. Although, the models and IMERG product underestimated or overestimated the amount of precipitation, but they were able to detect most extreme precipitation events. Overall, the results of this study show the IMERG precipitation estimates and NWP ensemble forecasts performed well in the three major flood events in spring 2019 in Iran. Given wide spread damages caused by the floods, the necessity of establishing an efficient flood warning system using the best precipitation products is advised.</p><p> </p>

Statistical Characteristics of Daily Precipitation: Comparisons of Gridded and Point Datasets

2008 ◽  
Vol 47 (9) ◽  
pp. 2468-2476 ◽  
Author(s):  
Leslie A. Ensor ◽  
Scott M. Robeson
Keyword(s):  
Daily Precipitation ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Statistical Characteristics ◽  
Precipitation Data ◽  
Spatial Coverage ◽  
Daily Precipitation Data ◽  
Precipitation Total ◽  
Grid Points ◽  
Precipitation Events

Abstract Gridding of daily precipitation data alleviates many of the limitations of data that are derived from point observations, such as problems associated with missing data and the lack of spatial coverage. As a result, gridded precipitation data can be valuable for applied climatological research and monitoring, but they too have limitations. To understand the limitations of gridded data more fully (especially when they are used as surrogates for station data), annual precipitation total, rain-day frequency, and annual maxima are calculated and compared for five Midwestern grid points from the Climate Prediction Center’s Unified Rain Gauge Dataset (URD) and those of its nearest (rain gauge) station. To further examine differences between the two datasets, return periods of daily precipitation were calculated over a region encompassing Illinois and Indiana. These analyses reveal that the gridding process used to create the URD produced nearly the same annual totals as the rain gauge data; however, the gridding significantly increased the frequency of low-precipitation events while greatly reducing the frequency of heavy-precipitation events. Extreme precipitation values also were greatly reduced in the gridded precipitation data. While smoothing nearly always occurs when data are gridded, the gridding of discrete variables such as daily precipitation can produce datasets with statistical characteristics that are very different from those of the original observations.

Temporal Changes in the Areal Coverage of Daily Extreme Precipitation in the Northeastern United States Using High-Resolution Gridded Data

2020 ◽  
Vol 59 (3) ◽  
pp. 551-565
Author(s):  
Arthur T. DeGaetano ◽  
Griffin Mooers ◽  
Thomas Favata
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Northeastern United States ◽  
Precipitation Frequency ◽  
Polygon Area ◽  
Areal Precipitation ◽  
Grid Points

AbstractTime-dependent changes in extreme precipitation occurrence across the northeastern United States are evaluated in terms of areal extent. Using gridded precipitation data for the period from 1950 to 2018, polygons are defined that are based on isohyets corresponding to extreme daily precipitation accumulations. Across the region, areal precipitation is characterized on the basis of the annual and seasonal number of extreme precipitation polygons and the area of the polygons. Using the subset of grid points that correspond to station locations in the northeastern United States, gridded precipitation replicates the observed trends in extreme precipitation based on station observations. Although the number of extreme precipitation polygons does not change significantly through time, there is a marked increase in the area covered by the polygons. The median annual polygon area nearly doubles from 1950 to 2013. Consistent results occur for percentiles other than the median and a range of extreme precipitation amount thresholds, with the most pronounced changes observed in spring and summer. Like trends in station data, outside the northeastern United States trends in extreme precipitation polygon area are negative, particularly in the western United States, or they are not statistically significant. Collectively, the results suggest that the increases in heavy precipitation frequency and amount observed at stations in the northeastern United States are a manifestation of an expansion of the spatial area over which extreme precipitation occurs rather than a change in the number of unique extreme precipitation polygons.

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

scholarly journals Temporal variations and change in forest fire danger in Europe for 1960–2012

2014 ◽  
Vol 14 (6) ◽  
pp. 1477-1490 ◽  
Author(s):  
A. Venäläinen ◽  
N. Korhonen ◽  
O. Hyvärinen ◽  
N. Koutsias ◽  
F. Xystrakis ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
National Level ◽  
Temporal Variations ◽  
Fire Danger ◽  
Fire Season ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Current Season ◽  
Forest Fire Danger

Abstract. Understanding how fire weather danger indices changed in the past and how such changes affected forest fire activity is important in a changing climate. We used the Canadian Fire Weather Index (FWI), calculated from two reanalysis data sets, ERA-40 and ERA Interim, to examine the temporal variation of forest fire danger in Europe in 1960–2012. Additionally, we used national forest fire statistics from Greece, Spain and Finland to examine the relationship between fire danger and fires. There is no obvious trend in fire danger for the time period covered by ERA-40 (1960–1999), whereas for the period 1980–2012 covered by ERA Interim, the mean FWI shows an increasing trend for southern and eastern Europe which is significant at the 99% confidence level. The cross correlations calculated at the national level in Greece, Spain and Finland between total area burned and mean FWI of the current season is of the order of 0.6, demonstrating the extent to which the current fire-season weather can explain forest fires. To summarize, fire risk is multifaceted, and while climate is a major determinant, other factors can contribute to it, either positively or negatively.

scholarly journals Influence of Precipitation Forcing Uncertainty on Hydrological Simulations with the NASA South Asia Land Data Assimilation System

Hydrology ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 57 ◽  
Author(s):  
Debjani Ghatak ◽  
Benjamin Zaitchik ◽  
Sujay Kumar ◽  
Mir A. Matin ◽  
Birendra Bajracharya ◽  
...  
Keyword(s):  
South Asia ◽  
Hydrological Simulation ◽  
Meteorological Forcing ◽  
In Situ Data ◽  
Precipitation Estimates ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

: Accurate meteorological estimates are critical for process-based hydrological simulation and prediction. This presents a significant challenge in mountainous Asia where in situ meteorological stations are limited and major river basins cross international borders. In this context, remotely sensed and model-derived meteorological estimates are often necessary inputs for distributed hydrological analysis. However, these datasets are difficult to evaluate on account of limited access to ground data. In this case, the implications of uncertainty associated with precipitation forcing for hydrological simulations is explored by driving the South Asia Land Data Assimilation System (South Asia LDAS) using a range of meteorological forcing products. MERRA2, GDAS, and CHIRPS produce a wide range of estimates for rainfall, which causes a widespread simulated streamflow and evapotranspiration. A combination of satellite-derived and limited in situ data are applied to evaluate model simulations and, by extension, to constrain the estimates of precipitation. The results show that available gridded precipitation estimates based on in situ data may systematically underestimate precipitation in mountainous regions and that performance of gridded satellite-derived or modeled precipitation estimates varies systematically across the region. Since no station-based data or product including station data is satisfactory everywhere, our results suggest that the evaluation of the hydrological simulation of streamflow and ET can be used as an indirect evaluation of precipitation forcing based on ground-based products or in-situ data. South Asia LDAS produces reasonable evapotranspiration and streamflow when forced with appropriate meteorological forcing and the choice of meteorological forcing should be made based on the geographical location as well as on the purpose of the simulations.

scholarly journals Djankuat glacier station in the North Caucasus, Russia: a database of glaciological, hydrological, and meteorological observations and stable isotope sampling results during 2007–2017

2019 ◽  
Vol 11 (3) ◽  
pp. 1463-1481 ◽  
Author(s):  
Ekaterina P. Rets ◽  
Viktor V. Popovnin ◽  
Pavel A. Toropov ◽  
Andrew M. Smirnov ◽  
Igor V. Tokarev ◽  
...  
Keyword(s):  
Wind Speed ◽  
Precipitation Amount ◽  
Turbulent Fluxes ◽  
Water Runoff ◽  
North Caucasus ◽  
Time Step ◽  
Glacier Surface ◽  
The North ◽  
Meteorological Observations ◽  
The Impact

Abstract. This study presents a dataset on long-term multidisciplinary glaciological, hydrological, and meteorological observations and isotope sampling in a sparsely monitored alpine zone of the North Caucasus in the Djankuat research basin. The Djankuat glacier, which is the largest in the basin, was chosen as representative of the central North Caucasus during the International Hydrological Decade and is one of 30 “reference” glaciers in the world that have annual mass balance series longer than 50 years (Zemp et al., 2009). The dataset features a comprehensive set of observations from 2007 to 2017 and contains yearly measurements of snow depth and density; measurements of dynamics of snow and ice melting; measurements of water runoff, conductivity, turbidity, temperature, δ18O, δD at the main gauging station (844 samples in total) with an hourly or sub-daily time step depending on the parameter; data on δ18O and δ2H sampling of liquid precipitation, snow, ice, firn, and groundwater in different parts of the watershed taken regularly during melting season (485 samples in total); measurements of precipitation amount, air temperature, relative humidity, shortwave incoming and reflected radiation, longwave downward and upward radiation, atmospheric pressure, and wind speed and direction – measured at several automatic weather stations within the basin with 15 min to 1 h time steps; gradient meteorological measurements to estimate turbulent fluxes of heat and moisture, measuring three components of wind speed at a frequency of 10 Hz to estimate the impulse of turbulent fluxes of sensible and latent heat over the glacier surface by the eddy covariance method. Data were collected during the ablation period (June–September). The observations were halted in winter. The dataset is available from PANGAEA (https://doi.org/10.1594/PANGAEA.894807, Rets et al., 2018a) and will be further updated. The dataset can be useful for developing and verifying hydrological, glaciological, and meteorological models for alpine areas, to study the impact of climate change on hydrology of mountain regions using isotopic and hydrochemical approaches in hydrology. As the dataset includes the measurements of hydrometeorological and glaciological variables during the catastrophic proglacial lake outburst in the neighboring Bashkara valley in September 2017, it is a valuable contribution to study lake outbursts.

Sign in / Sign up

Export Citation Format

Share Document