Sensitivity of Surface Air Temperature Analyses to Background and Observation Errors

Tyndall, Daniel P.; Horel, John D.; Pondeca, Manuel

doi:10.1175/2009waf2222304.1

Cited by 13 publications

(22 citation statements)

References 14 publications

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“…It should be noticed that the B matrix is specified in a different way compared to other studies (see Myrick et al, 2005;Tyndall et al, 2010 and references therein), which explicitly introduce the horizontal (R) and vertical (R z ) decorrelation distances and assume an exponential decrease of the background correlation error with distance. There are two 29 and maximum temperatures) are 6 th order polynomial fittings of the binned covariances.…”

Section: The Analysis Systemmentioning

confidence: 99%

Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

Federico

2011

Nat. Hazards Earth Syst. Sci.

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Abstract. Since 2005, one-hour temperature forecasts for the Calabria region (southern Italy), modelled by the Regional Atmospheric Modeling System (RAMS), have been issued by CRATI/ISAC-CNR (Consortium for Research and Application of Innovative Technologies/Institute for Atmospheric and Climate Sciences of the National Research Council) and are available online at meteo.crati.it/previsioni.html (every six hours). Beginning in June 2008, the horizontal resolution was enhanced to 2.5 km. In the present paper, forecast skill and accuracy are evaluated out to four days for the 2008 summer season (from 6 June to 30 September, 112 runs). For this purpose, gridded high horizontal resolution forecasts of minimum, mean, and maximum temperatures are evaluated against gridded analyses at the same horizontal resolution (2.5 km).Gridded analysis is based on Optimal Interpolation (OI) and uses the RAMS first-day temperature forecast as the background field. Observations from 87 thermometers are used in the analysis system. The analysis error is introduced to quantify the effect of using the RAMS first-day forecast as the background field in the OI analyses and to define the forecast error unambiguously, while spatial interpolation (SI) analysis is considered to quantify the statistics' sensitivity to the verifying analysis and to show the quality of the OI analyses for different background fields.Two case studies, the first one with a low (less than the 10th percentile) root mean square error (RMSE) in the OI analysis, the second with the largest RMSE of the whole period in the OI analysis, are discussed to show the forecast performance under two different conditions. Cumulative statistics are used to quantify forecast errors out to four days. Results show that maximum temperature has the largest RMSE, while minimum and mean temperature errors are similar. ForCorrespondence to: S. Federico (s.federico@isac.cnr.it) the period considered, the OI analysis RMSEs for minimum, mean, and maximum temperatures vary from 1.8, 1.6, and 2.0 • C, respectively, for the first-day forecast, to 2.0, 1.9, and 2.6 • C, respectively, for the fourth-day forecast.Cumulative statistics are computed using both SI and OI analysis as reference. Although SI statistics likely overestimate the forecast error because they ignore the observational error, the study shows that the difference between OI and SI statistics is less than the analysis error.The forecast skill is compared with that of the persistence forecast. The Anomaly Correlation Coefficient (ACC) shows that the model forecast is useful for all days and parameters considered here, and it is able to capture day-to-day weather variability. The model forecast issued for the fourth day is still better than the first-day forecast of a 24-h persistence forecast, at least for mean and maximum temperature. The impact of using the RAMS first-day forecast as the background field in the OI analysis is quantified by comparing statistics computed with OI and SI analyses. Minimum temperature is more sensiti...

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Section: The Analysis Systemmentioning

confidence: 99%

Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

Federico

2011

Nat. Hazards Earth Syst. Sci.

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“…Specific concerns over the Intermountain West include 1) very limited use of satellite radiance data due to uncertainties in land surface emissivity, especially over high and irregular orography (Derber and Wu 1998;McNally et al 2000;McNally et al 2006); 2) the low density and limited use-representativeness of surface observations at the scales resolved by the three reanalyses (Horel et al 2002;Myrick et al 2005;Myrick and Horel 2008;Tyndall et al 2010); and 3) model terrain representation, which influences the background analysis to which corrections are applied.…”

Section: A Reanalysis Datamentioning

confidence: 99%

Multi-Reanalysis Climatology of Intermountain Cyclones

Jeglum

Steenburgh

Lee

et al. 2010

Monthly Weather Review

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The topography in and around the Intermountain West strongly affects the genesis, migration, and lysis of extratropical cyclones. Here intermountain (i.e., Nevada or Great Basin) cyclone (IC) activity and evolution are examined using the ECMWF Re-Analysis Interim (ERA-Interim) the North American Regional Reanalysis (NARR), and the NCEP-NCAR reanalysis from 1989 to 2008, the period during which all three are available. The ICs are defined and tracked objectively as 850-hPa geopotential height depressions of $40 m that persist for $12 h.The monthly distribution of IC center and genesis frequency in all three reanalyses is bimodal with spring (absolute) and fall (secondary) maxima. Although the results are sensitive to differences in resolution, topographic representation, and reanalysis methodology, both the ERA-Interim and NARR produce frequent IC centers and genesis in the Great Basin cyclone region, which extends from the southern ''high'' Sierra to northwest Utah, and the Canyonlands cyclone region, which lies over the upper Colorado River basin of southeast Utah. The NCEP-NCAR reanalysis fails to resolve these two distinct cyclone regions and produces less frequent IC centers and genesis than the ERA-Interim and NARR.An ERA-Interim-based composite of strong ICs generated in cross-Sierra (2108-3008) 500-hPa flow shows that cyclogenesis is preceded by the development of the Great Basin confluence zone (GBCZ), a regional airstream boundary that extends downstream from the Sierra Nevada across the Intermountain West. Cyclogenesis occurs along the GBCZ as large-scale ascent develops over the Intermountain West in advance of an approaching upper-level trough. Flow splitting around the high Sierra and the presence of low-level baroclinicity along the GBCZ suggest that IC evolution may be better conceptualized from a potential vorticity perspective than from traditional quasigeostrophic models of lee cyclogenesis. Although these results provide new insights into IC activity and evolution, analysis uncertainty and the cyclone identification criteria are important sources of ambiguity that cannot be fully eliminated.

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“…For RTMA applications, the desired properties of representativeness and nonredundancy are achieved fairly well and efficiently with the help of a simple application of a continuous space-filling Hilbert curve (De Pondeca et al 2006;Purser et al 2009;Tyndall et al 2010). Cross validation denotes the use of a subset of the observed data to verify the analysis performed with these selected data withheld.…”

Section: Average Cross-validation Statisticsmentioning

confidence: 99%

“…2) Exploring the use of time-dependent autocovariances built from background field diagnostics, such as background potential temperature. For example, Tyndall et al (2010) incorporate cross validation in their estimates of the background error variance of the RTMA temperature background fields as well as the observation error variance as a function of observation type (their Table 1). 4) Improving the estimate of the analysis uncertainty to remove the ambiguity in the amplitude and at the same time incorporate information about the systematic errors from the background and the observations.…”

Section: B Ongoing Workmentioning

confidence: 99%

The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development

Pondeca

Manikin

DiMego

et al. 2011

Weather and Forecasting

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113

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In 2006, the National Centers for Environmental Prediction (NCEP) implemented the Real-Time Mesoscale Analysis (RTMA) in collaboration with the Earth System Research Laboratory and the National Environmental, Satellite, and Data Information Service (NESDIS). In this work, a description of the RTMA applied to the 5-km resolution conterminous U.S. grid of the National Digital Forecast Database is given. Its two-dimensional variational data assimilation (2DVAR) component used to analyze near-surface observations is described in detail, and a brief discussion of the remapping of the NCEP stage II quantitative precipitation amount and NESDIS Geostationary Operational Environmental Satellite (GOES) sounder effective cloud amount to the 5-km grid is offered. Terrain-following background error covariances are used with the 2DVAR approach, which produces gridded fields of 2-m temperature, 2-m specific humidity, 2-m dewpoint, 10-m U and V wind components, and surface pressure. The estimate of the analysis uncertainty via the Lanczos method is briefly described. The strength of the 2DVAR is illustrated by (i) its ability to analyze a June 2007 cold temperature pool over the Washington, D.C., area; (ii) its fairly good analysis of a December 2008 mid-Atlantic region high-wind event that started from a very weak first guess; and (iii) its successful recovery of the finescale moisture features in a January 2010 case study over southern California. According to a cross-validation analysis for a 15-day period during November 2009, root-mean-square error improvements over the first guess range from 16% for wind speed to 45% for specific humidity.

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Sensitivity of Surface Air Temperature Analyses to Background and Observation Errors

Cited by 13 publications

References 14 publications

Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

Multi-Reanalysis Climatology of Intermountain Cyclones

The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development

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