Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations

Behrangi, Ali; Singh, Alka; Song, Yang; Panahi, Milad

doi:10.1029/2019gl084221

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…In Finland, winter (November-March) precipitation from ERA5-Land were found to be overestimated by 12% in Helsinki and by 18% in Sodankylä 76 compared to observations, but these discrepancies may be explained by the rain gauge undercatch which is prominent, especially in winter, when precipitation is mostly solid 78 . Despite these slight biases in precipitation, Räisänen 76 reported high interannual correlation (0.91-0.96) between ERA5-Land predictions and precipitation observed at meteorological stations in winter, even though ERA5 (and thus ERA5-Land) does not assimilate precipitation observations from Europe 23 .…”

Section: Technical Validationmentioning

confidence: 76%

Bioclimatic atlas of the terrestrial Arctic

et al. 2023

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The Arctic is the region on Earth that is warming at the fastest rate. In addition to rising means of temperature-related variables, Arctic ecosystems are affected by increasingly frequent extreme weather events causing disturbance to Arctic ecosystems. Here, we introduce a new dataset of bioclimatic indices relevant for investigating the changes of Arctic terrestrial ecosystems. The dataset, called ARCLIM, consists of several climate and event-type indices for the northern high-latitude land areas > 45°N. The indices are calculated from the hourly ERA5-Land reanalysis data for 1950–2021 in a spatial grid of 0.1 degree (~9 km) resolution. The indices are provided in three subsets: (1) the annual values during 1950–2021; (2) the average conditions for the 1991–2020 climatology; and (3) temporal trends over 1951–2021. The 72-year time series of various climate and event-type indices draws a comprehensive picture of the occurrence and recurrence of extreme weather events and climate variability of the changing Arctic bioclimate.

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Section: Technical Validationmentioning

confidence: 76%

Bioclimatic atlas of the terrestrial Arctic

et al. 2023

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“…Using the observation of mass change from the Gravity Recovery and Climate Experiment (GRACE), [24] calculated snowfall accumulation over cold regions in the northern hemisphere and used the values to assess two popular gauge-undercatch correction factors (CFs): Legates climatology (CF-L), utilized in GPCP, and Fuchs dynamic correction model (CF-F), used in the Global Precipitation Climatology Centre (GPCC) monitoring product [25]. The difference between the two CFs can exceed 50% [24,26], so selection of the more accurate CF is important. Their results showed a greater consistency between GRACE-based snow accumulation estimates and GPCC-F (GPCC corrected by CF-F) than GPCC-L (GPCC corrected by CF-L), in terms of both the amount and spatial pattern of snowfall accumulation over the studied regions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

et al. 2021

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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.

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“…Using the observation of mass change from the Gravity Recovery and Climate Experiment (GRACE), Behrangi et al (2018a) calculated snowfall accumulation over cold regions in the northern hemisphere and used the values to assess two popular gauge-undercatch correction factors (CFs): Legates climatology (CF-L) utilized in GPCP, and Fuchs dynamic correction model (CF-F) used in the Global Precipitation Climatology Centre (GPCC) monitoring product (Schneider et al, 2017). The difference between the two CFs can exceed 50% (Behrangi et al, 2018a(Behrangi et al, , 2019, so selection of the more accurate CF is important. Their results show a greater consistency between GRACE-based snow accumulation estimate and GPCC-F (GPCC corrected by CF-F) than GPCC-L (GPCC corrected by CF-L), in terms of both amount and spatial pattern of snowfall accumulation over the studied regions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

Song¹,

Broxton²,

Ehsani³

et al. 2021

Preprint

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The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 Snow Telemetry (SNOTEL) sites in Alaska are 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 the high resolution radar/rain gauge data (Stage IV) product was also utilized to add insights into scaling differences between various products. The outcomes were also 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 in which other products can be assessed. 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 tend to overestimate snow accumulation in the study area, while current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that corrections factors applied to rain gauges are effective in 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 in capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill in capturing the geographical distribution of snowfall and precipitation accumulation, so 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.

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Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations

Cited by 12 publications

References 32 publications

Bioclimatic atlas of the terrestrial Arctic

Bioclimatic atlas of the terrestrial Arctic

Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

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