A radar‐based rainfall climatology of Great Britain and Ireland

Fairman, Jonathan G.; Schultz, David M.; Kirshbaum, Daniel J.; Gray, Suzanne L.; Barrett, Andrew I.

doi:10.1002/wea.2486

Cited by 45 publications

(49 citation statements)

References 32 publications

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“…Are there systematic biases in numerical model output related to the rainshadow effect (e.g., Colle et al 2000)? Anecdotal evidence from our real-time model simulations at ManUniCast.com (Schultz et al 2015) suggests the rain-shadow effect is underrepresented in the model output in some cases. Do leeside mountain waves produce strong descent immediately to the lee of the Peak District that enhances the gradient in precipitation, or does leeside stability affect the distribution of precipitation (e.g., Brady and Waldstreicher 2001;Smith et al 2009)?…”

Section: Documentarymentioning

confidence: 99%

Quantifying the Rain-Shadow Effect: Results from the Peak District, British Isles

Stockham

Schultz

Fairman

et al. 2018

Bulletin of the American Meteorological Society

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Although rain shadows (i.e., leeside reductions of precipitation downwind of orography) are commonly described in textbooks, quantitative climatologies of the rain-shadow effect are rare. To test quantitatively a classic rain-shadow locality of the Peak District, United Kingdom, precipitation from 54 observing stations over 30 years (1981–2010) are examined. Under 850-hPa westerlies, annual and daily precipitation amounts are on average higher in Manchester in the west and the Peak District than in Sheffield in the east. More precipitation falls—and falls more frequently—frequently in Manchester than Sheffield on 197 westerly flow days annually. In contrast, more precipitation falls—and falls more frequently—in Sheffield than Manchester on 28 easterly flow days annually. These bulk precipitation statistics support a climatological rain shadow. However, when individual days are investigated, only 17% of westerly flow days occur where daily rainfall data might exhibit the rain-shadow effect (defined here as Manchester with precipitation and Sheffield with no precipitation). In contrast, only 10% of easterly flow days occur where daily rainfall data might exhibit the rain-shadow effect (Sheffield with precipitation and Manchester with no precipitation). Thus, westerly winds are more likely to exhibit a rain-shadow effect than easterly winds. Although the distribution of precipitation observed across the Peak District can sometimes be explained by the rain-shadow effect, the occurrence of the rain-shadow effect by our admittedly strict definition is not as frequent as one might expect to explain the local precipitation climate for which it has sometimes been previously credited. Thus, an attempt to understand the climatological relevance of the rain-shadow effect from one location reveals ambiguity in the definition of a rain shadow and in its interpretation from real rainfall data.

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Section: Documentarymentioning

confidence: 99%

Quantifying the Rain-Shadow Effect: Results from the Peak District, British Isles

Stockham

Schultz

Fairman

et al. 2018

Bulletin of the American Meteorological Society

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“…The analyzed parameters include  High-resolution maps of annual average precipitation and frequency of precipitation rate (Fairman et al, 2015)  Exceedance probability of 1h rainfall (Overeem et al, 2009) …”

Section: Examples Of Data Usementioning

confidence: 99%

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

Vos¹,

Leijnse²,

Uijlenhoet³

2018

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“…The season‐averaged spatial distribution of precipitation agrees with previously published UK precipitation climatologies (e.g. Warren, ; Fairman et al , ).…”

Section: Data and Modelmentioning

confidence: 99%

Assessing spatial precipitation uncertainties in a convective‐scale ensemble

Dey

Plant

Roberts³

et al. 2016

Quart J Royal Meteoro Soc

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New techniques have recently been developed to quantify the location‐dependent spatial agreement between ensemble members, and the spatial spread–skill relationship. In this article a summer of convection‐permitting ensemble forecasts are analysed to better understand the factors influencing location‐dependent spatial agreement of precipitation fields and the spatial spread–skill relationship over the UK. The aim is to further investigate the agreement scale method, and to highlight the information that could be extracted for a more long‐term routine model evaluation. Overall, for summer 2013, the UK 2.2 km grid spacing ensemble system was found to be reasonably well spread spatially, although there was a tendency for the ensemble to be overconfident in the location of precipitation. For the forecast lead times considered (up to 36 h), a diurnal cycle was seen in the spatial agreement and in the spatial spread–skill relationship: the forecast spread and error did not increase noticeably with forecast lead time. Both the spatial agreement and the spatial spread–skill were dependent on the fractional coverage and average intensity of precipitation. A poor spread–skill relationship was associated with a low fractional coverage of rain and low average rain rates. The times with a smaller fractional coverage, or lower intensity, of precipitation were found to have lower spatial agreement. The spatial agreement was found to be location dependant, with higher confidence in the location of precipitation to the northwest of the UK.

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A radar‐based rainfall climatology of Great Britain and Ireland

Cited by 45 publications

References 32 publications

Quantifying the Rain-Shadow Effect: Results from the Peak District, British Isles

Quantifying the Rain-Shadow Effect: Results from the Peak District, British Isles

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

Assessing spatial precipitation uncertainties in a convective‐scale ensemble

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