Improving Observations of Precipitation Type at the Surface: A 5-year Verification of a Radar-derived Product from the United Kingdom Met Office.

AbstractThis study aims to verify the skill of a radar-based surface precipitation type (SPT) product with observations on the ground. Social and economic impacts can occur from SPT because it is not well forecast or observed. Observations from the United Kingdom Meteorological Office’s weather radar network are combined with post-processed numerical weather prediction (NWP) freezing level heights in a Boolean logic algorithm to create a 1 km resolution cartesian-gridded map of SPT. Here 5 years of discrete non-probabilistic outputs of rain, mixed phase, and snow are compared against surface observations made by trained observers, automatic weather stations, and laser disdrometers. The novel skill verification method developed as part of this study employs several tolerances of space and time from the SPT product, indicating the precision of the product for a desired accuracy. In general the results indicate that the tolerance verification method works well and produces reasonable statistical score ranges grounded in physical constraints. Using this method, we find that the mixed precipitation class is the least well diagnosed which is due to a negative bias in the input temperature height field, resulting in rain events frequently being classified as mixed. Snowis capturedwell by the product which is entirely reliant upon a post-processed NWP temperature field, although a single period of anomalously cold temperatures positively skewed snow scores with low-skill events. Furthermore, we conclude that more verification consistency is needed amongst studies to help identify successful approaches and thus improve SPT forecasts.

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Calibration and evaluation of the Canadian Forest Fire Weather Index (FWI) System for improved wildland fire danger rating in the United Kingdom

Natural Hazards and Earth System Science ◽

10.5194/nhess-16-1217-2016 ◽

2016 ◽

Vol 16 (5) ◽

pp. 1217-1237 ◽

Cited By ~ 13

Author(s):

Mark C. de Jong ◽

Martin J. Wooster ◽

Karl Kitchen ◽

Cathy Manley ◽

Rob Gazzard ◽

...

Keyword(s):

United Kingdom ◽

Fire Severity ◽

Weather Prediction ◽

Fire Danger ◽

Fire Weather ◽

Rating Systems ◽

Weather Index ◽

Fire Weather Index ◽

The United Kingdom ◽

The Uk

Abstract. Wildfires in the United Kingdom (UK) pose a threat to people, infrastructure and the natural environment. During periods of particularly fire-prone weather, wildfires can occur simultaneously across large areas, placing considerable stress upon the resources of fire and rescue services. Fire danger rating systems (FDRSs) attempt to anticipate periods of heightened fire risk, primarily for early-warning and preparedness purposes. The UK FDRS, termed the Met Office Fire Severity Index (MOFSI), is based on the Fire Weather Index (FWI) component of the Canadian Forest FWI System. The MOFSI currently provides daily operational mapping of landscape fire danger across England and Wales using a simple thresholding of the final FWI component of the Canadian FWI System. However, it is known that the system has scope for improvement. Here we explore a climatology of the six FWI System components across the UK (i.e. extending to Scotland and Northern Ireland), calculated from daily 2km × 2km gridded numerical weather prediction data and supplemented by long-term meteorological station observations. We used this climatology to develop a percentile-based calibration of the FWI System, optimised for UK conditions. We find this approach to be well justified, as the values of the "raw" uncalibrated FWI components corresponding to a very "extreme" (99th percentile) fire danger situation vary by more than an order of magnitude across the country. Therefore, a simple thresholding of the uncalibrated component values (as is currently applied in the MOFSI) may incur large errors of omission and commission with respect to the identification of periods of significantly elevated fire danger. We evaluate our approach to enhancing UK fire danger rating using records of wildfire occurrence and find that the Fine Fuel Moisture Code (FFMC), Initial Spread Index (ISI) and FWI components of the FWI System generally have the greatest predictive skill for landscape fire activity across Great Britain, with performance varying seasonally and by land cover type. At the height of the most recent severe wildfire period in the UK (2 May 2011), 50 % of all wildfires occurred in areas where the FWI component exceeded the 99th percentile. When all wildfire events during the 2010–2012 period are considered, the 75th, 90th and 99th percentiles of at least one FWI component were exceeded during 85, 61 and 18 % of all wildfires respectively. Overall, we demonstrate the significant advantages of using a percentile-based calibration approach for classifying UK fire danger, and believe that our findings provide useful insights for future development of the current operational MOFSI UK FDRS.

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Inherent Bounds on Forecast Accuracy due to Observation Uncertainty Caused by Temporal Sampling

Monthly Weather Review ◽

10.1175/mwr-d-15-0173.1 ◽

2015 ◽

Vol 143 (10) ◽

pp. 4236-4243 ◽

Cited By ~ 6

Author(s):

Marion P. Mittermaier ◽

David B. Stephenson

Keyword(s):

United Kingdom ◽

Measurement Errors ◽

Total Error ◽

Weather Prediction ◽

Forecast Accuracy ◽

Spatial Location ◽

Forecast Errors ◽

Temporal Sampling ◽

The United Kingdom ◽

Temperature Observations

Abstract Synoptic observations are often treated as error-free representations of the true state of the real world. For example, when observations are used to verify numerical weather prediction (NWP) forecasts, forecast–observation differences (the total error) are often entirely attributed to forecast inaccuracy. Such simplification is no longer justifiable for short-lead forecasts made with increasingly accurate higher-resolution models. For example, at least 25% of t + 6 h individual Met Office site-specific (postprocessed) temperature forecasts now typically have total errors of less than 0.2 K, which are comparable to typical instrument measurement errors of around 0.1 K. In addition to instrument errors, uncertainty is introduced by measurements not being taken concurrently with the forecasts. For example, synoptic temperature observations in the United Kingdom are typically taken 10 min before the hour, whereas forecasts are generally extracted as instantaneous values on the hour. This study develops a simple yet robust statistical modeling procedure for assessing how serially correlated subhourly variations limit the forecast accuracy that can be achieved. The methodology is demonstrated by application to synoptic temperature observations sampled every minute at several locations around the United Kingdom. Results show that subhourly variations lead to sizeable forecast errors of 0.16–0.44 K for observations taken 10 min before the forecast issue time. The magnitude of this error depends on spatial location and the annual cycle, with the greater errors occurring in the warmer seasons and at inland sites. This important source of uncertainty consists of a bias due to the diurnal cycle, plus irreducible uncertainty due to unpredictable subhourly variations that fundamentally limit forecast accuracy.

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