Local Adjustment of the Background Error Correlation for Surface Analyses over Complex Terrain

2005 ◽  
Vol 20 (2) ◽  
pp. 149-160 ◽  
Author(s):  
David T. Myrick ◽  
John D. Horel ◽  
Steven M. Lazarus
Keyword(s):  
Surface Temperature ◽  
Correlation Matrix ◽  
Analytic Solution ◽  
Weather Conditions ◽  
Optimal Interpolation ◽  
Unbiased Estimation ◽  
Background Error ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Error Correlation

Abstract The terrain between grid points is used to modify locally the background error correlation matrix in an objective analysis system. This modification helps to reduce the influence across mountain barriers of corrections to the background field that are derived from surface observations. This change to the background error correlation matrix is tested using an analytic case of surface temperature that encapsulates the significant features of nocturnal radiation inversions in mountain basins, which can be difficult to analyze because of locally sharp gradients in temperature. Bratseth successive corrections, optimal interpolation, and three-dimensional variational approaches are shown to yield exactly the same surface temperature analysis. Adding the intervening terrain term to the Bratseth approach led to solutions that match more closely the specified analytic solution. In addition, the convergence of the Bratseth solutions to the best linear unbiased estimation of the analytic solution is faster. The intervening terrain term was evaluated in objective analyses over the western United States derived from a modified version of the Advanced Regional Prediction System Data Assimilation System. Local adjustment of the background error correlation matrix led to improved surface temperature analyses by limiting the influence of observations in mountain valleys that may differ from the weather conditions present in adjacent valleys.

scholarly journals Verification of Surface Temperature Forecasts from the National Digital Forecast Database over the Western United States

2006 ◽  
Vol 21 (5) ◽  
pp. 869-892 ◽  
Author(s):  
David T. Myrick ◽  
John D. Horel
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Winter Season ◽  
Forecast Accuracy ◽  
Horizontal Resolution ◽  
Western United States ◽  
Lead Times ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Mean Square Errors

Abstract Experimental gridded forecasts of surface temperature issued by National Weather Service offices in the western United States during the 2003/04 winter season (18 November 2003–29 February 2004) are evaluated relative to surface observations and gridded analyses. The 5-km horizontal resolution gridded forecasts issued at 0000 UTC for forecast lead times at 12-h intervals from 12 to 168 h were obtained from the National Digital Forecast Database (NDFD). Forecast accuracy and skill are determined relative to observations at over 3000 locations archived by MesoWest. Forecast quality is also determined relative to Rapid Update Cycle (RUC) analyses at 20-km resolution that are interpolated to the 5-km NDFD grid as well as objective analyses obtained from the Advanced Regional Prediction System Data Assimilation System that rely upon the MesoWest observations and RUC analyses. For the West as a whole, the experimental temperature forecasts issued at 0000 UTC during the 2003/04 winter season exhibit skill at lead times of 12, 24, 36, and 48 h on the basis of several verification approaches. Subgrid-scale temperature variations and observational and analysis errors undoubtedly contribute some uncertainty regarding these results. Even though the “true” values appropriate to evaluate the forecast values on the NDFD grid are unknown, it is estimated that the root-mean-square errors of the NDFD temperature forecasts are on the order of 3°C at lead times shorter than 48 h and greater than 4°C at lead times longer than 120 h. However, such estimates are derived from only a small fraction of the NDFD grid boxes. Incremental improvements in forecast accuracy as a result of forecaster adjustments to the 0000 UTC temperature grids from 144- to 24-h lead times are estimated to be on the order of 13%.

Observations and Numerical Simulation of Upper Boundary Layer Rapid Drying and Moistening Events during the International H2O Project (IHOP_2002)

2008 ◽  
Vol 136 (8) ◽  
pp. 3106-3120 ◽  
Author(s):  
Robin L. Tanamachi ◽  
Wayne F. Feltz ◽  
Ming Xue
Keyword(s):  
Boundary Layer ◽  
Weather Conditions ◽  
Cold Pool ◽  
High Temporal Resolution ◽  
Advanced Regional Prediction System ◽  
The North ◽  
Time Period ◽  
Regional Prediction ◽  
Mesoscale Convective ◽  
Upper Boundary

Abstract On the morning of 12 June 2002, a series of upper boundary layer (UBL) rapid drying and moistening events (RDEs and RMEs, respectively) occurred at the “Homestead” site of the International H2O Project (IHOP_2002). Over a period of 10 h, atmospheric water vapor in the UBL decreased or increased within a matter of minutes four separate times. High-temporal-resolution data of the RDEs and RMEs collected by numerous instruments deployed for this intensive observation period are presented. The results of an Advanced Regional Prediction System (ARPS) simulation of the weather conditions around the time period reproduced one of the two RDE–RME pairs with reasonably accurate amplitude and timing. Both the observational data and ARPS numerical model output indicate that the second RDE–RME pair resulted from the interaction between a dry air mass descending from the Rocky Mountains and a cold pool–internal undular bore couplet propagating over the Homestead site from a mesoscale convective complex to the north. The RDEs and RMEs, which were rarely observed during IHOP_2002, are believed to be an indirect indicator of such bores.

scholarly journals Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

2009 ◽  
Vol 48 (9) ◽  
pp. 1790-1802 ◽  
Author(s):  
David P. Duda ◽  
Patrick Minnis
Keyword(s):  
Relative Humidity ◽  
Wind Direction ◽  
Meteorological Data ◽  
Logistic Models ◽  
Satellite Observations ◽  
Lapse Rate ◽  
Prediction System ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Surface Models

Abstract A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.

scholarly journals Explicit Prediction of Hail in a Long-Lasting Multicellular Convective System in Eastern China Using Multimoment Microphysics Schemes

2018 ◽  
Vol 75 (9) ◽  
pp. 3115-3137 ◽  
Author(s):  
Liping Luo ◽  
Ming Xue ◽  
Kefeng Zhu ◽  
Bowen Zhou
Keyword(s):  
Eastern China ◽  
Convective System ◽  
Distribution Characteristics ◽  
Grid Spacing ◽  
Particle Size Distributions ◽  
Growth Processes ◽  
Microphysics Scheme ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Size Sorting

Abstract During the afternoon of 28 April 2015, a multicellular convective system swept southward through much of Jiangsu Province, China, over about 7 h, producing egg-sized hailstones on the ground. The hailstorm event is simulated using the Advanced Regional Prediction System (ARPS) at 1-km grid spacing. Different configurations of the Milbrandt–Yau microphysics scheme are used, predicting one, two, and three moments of the hydrometeor particle size distributions (PSDs). Simulated reflectivity and maximum estimated size of hail (MESH) derived from the simulations are verified against reflectivity observed by operational S-band Doppler radars and radar-derived MESH, respectively. Comparisons suggest that the general evolution of the hailstorm is better predicted by the three-moment scheme, and neighborhood-based MESH evaluation further confirms the advantage of the three-moment scheme in hail size prediction. Surface accumulated hail mass, number, and hail distribution characteristics within simulated storms are examined across sensitivity experiments. Results suggest that multimoment schemes produce more realistic hail distribution characteristics, with the three-moment scheme performing the best. Size sorting is found to play a significant role in determining hail distribution within the storms. Detailed microphysical budget analyses are conducted for each experiment, and results indicate that the differences in hail growth processes among the experiments can be mainly ascribed to the different treatments of the shape parameter within different microphysics schemes. Both the differences in size sorting and hail growth processes contribute to the simulated hail distribution differences within storms and at the surface.

Modelling pavement temperature for winter maintenance operations

2004 ◽  
Vol 31 (2) ◽  
pp. 369-378 ◽  
Author(s):  
Aly Sherif ◽  
Yasser Hassan
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Negative Impact ◽  
Weather Conditions ◽  
Statistical Modelling ◽  
Dew Point ◽  
Road Maintenance ◽  
Winter Maintenance ◽  
Pavement Surface ◽  
Pavement Temperature

Road and highway maintenance is vital for the safety of citizens and for enabling emergency and security services to perform their essential functions. Accumulation of snow and (or) ice on the pavement surface during the wintertime substantially increases the risk of road crashes and can have negative impact on the economy of the region. Recently, road maintenance engineers have used pavement surface temperature as a guide to the application of deicers. Stations for road weather information systems (RWIS) have been installed across Europe and North America to collect data that can be used to predict weather conditions such as air temperature. Modelling pavement surface temperature as a function of such weather conditions (air temperature, dew point, relative humidity, and wind speed) can provide an additional component that is essential for winter maintenance operations. This paper uses data collected by RWIS stations at the City of Ottawa to device a procedure that maximizes the use of a data batch containing complete, partially complete, and unusable data and to study the relationship between the pavement surface temperature and weather variables. Statistical models were developed, where stepwise regression was first applied to eliminate those variables whose estimated coefficients are not statistically significant. The remaining variables were further examined according to their contribution to the criterion of best fit and their physical relationships to each other to eliminate multicollinearities. The models were further corrected for the autocorrelation in their error structures. The final version of the developed models may then be used as a part of the decision-making process for winter maintenance operations.Key words: winter maintenance, pavement temperature, statistical modelling, RWIS.

scholarly journals Spatial analysis of hourly precipitation combining observations and ensemble forecasts

2021 ◽  
Author(s):  
Cristian Lussana ◽  
Thomas N. Nipen ◽  
Ivar A. Seierstad ◽  
Christoffer A. Elo
Keyword(s):  
Spatial Analysis ◽  
Gamma Distribution ◽  
Numerical Models ◽  
Spatial Scales ◽  
Data Sources ◽  
Optimal Interpolation ◽  
Interpolation Scheme ◽  
Background Error ◽  
Hourly Precipitation ◽  
Gaussian Anamorphosis

<p>Hourly precipitation is often simultaneously simulated by numerical models and observed by multiple data sources. Accurate precipitation fields based on all available information are valuable input for numerous applications and a critical aspect of climate monitoring. </p><p>Inverse problem theory offers an ideal framework for the combination of observations with a numerical model background. In particular, we have considered a modified ensemble optimal interpolation scheme. The deviations between background and observations are used to adjust for deficiencies in the ensemble. A data transformation based on Gaussian anamorphosis has been used to optimally exploit the potential of the spatial analysis, given that precipitation is approximated with a gamma distribution and the spatial analysis requires normally distributed variables. For each point, the spatial analysis returns the shape and rate parameters of its gamma distribution. </p><p>The ensemble-based statistical interpolation scheme with Gaussian anamorphosis for precipitation (EnSI-GAP) is implemented in a way that the covariance matrices are locally stationary, and the background error covariance matrix undergoes a localization process. Concepts and methods that are usually found in data assimilation are here applied to spatial analysis, where they have been adapted in an original way to represent precipitation at finer spatial scales than those resolved by the background, at least where the observational network is dense enough.</p><p>The EnSI-GAP setup requires the specification of a restricted number of parameters, and specifically, the explicit values of the error variances are not needed, since they are inferred from the available data. </p><p>The examples of applications presented over Norway provide a better understanding of EnSI-GAP. The data sources considered are those typically used at national meteorological services, such as local area models, weather radars, and in situ observations. For this last data source, measurements from both traditional and opportunistic sensors have been considered.</p>

scholarly journals Sea surface temperature variations in the south-eastern BalticSea in 1960–2015

Baltica ◽  
2018 ◽  
Vol 30 (2) ◽  
pp. 75-85 ◽  
Author(s):  
Viktorija Rukšėnienė ◽  
Inga Dailidienė ◽  
Loreta Kelpšaitė-Rimkienė ◽  
Tarmo Soomere
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Air Temperature ◽  
Weather Conditions ◽  
Sea Surface ◽  
South Eastern ◽  
Air Temperatures ◽  
Joint Application ◽  
Open Sea ◽  
And Cluster Analysis

This study focuses on time scales and spatial variations of interrelations between average weather conditions and sea surface temperature (SST), and long-term changes in the SST in south-eastern Baltic Sea. The analysis relies on SST samples measured in situ four times a year in up to 17 open sea monitoring stations in Lithuanian waters in 1960–2015. A joint application of non-metric multi-dimensional scaling and cluster analysis reveals four distinct SST regimes and associated sub-regions in the study area. The increase in SST has occurred during both winter and summer seasons in 1960–2015 whereas the switch from relatively warm summer to colder autumn temperatures has been shifted by 4–6 weeks over this time in all sub-regions. The annual average air temperature and SST have increased by 0.03°C yr–1 and 0.02°C yr–1, respectively, from 1960 till 2015. These data are compared with air temperatures measured in coastal meteorological stations and averaged over time intervals from 1 to 9 weeks. Statistically significant positive correlation exists between the SST and the average air temperature. This correlation is strongest for the averaging interval of 35 days.

scholarly journals Perceived Influence of Weather Conditions on Rheumatic Pain in Romania

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Adina-Eliza Croitoru ◽  
Gabriela Dogaru ◽  
Titus Cristian Man ◽  
Simona Mălăescu ◽  
Marieta Motricală ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Pain Intensity ◽  
Air Temperature ◽  
Correlation Matrix ◽  
Great Majority ◽  
Weather Conditions ◽  
Weather Variables ◽  
Rheumatic Pain ◽  
Atmospheric Fronts ◽  
The Relationship

The main objective of this study was to analyze the perception of the influence of various weather conditions on patients with rheumatic pathology. A group of 394 patients, aged between 39 and 87 years and diagnosed with degenerative rheumatic diseases, were interviewed individually by using a questionnaire created specifically for this study. Further on, to assess the relationship between pain intensity and weather conditions, a frequency analysis based on Pearson’s correlation matrix was employed. The most important results are as follows: the great majority of the participants (more than 75%) believe that their rheumatic pain is definitely or to a great extent influenced by different weather conditions; most of the patients reported intensification of their pain with weather worsening, especially when cloudiness and humidity suddenly increase (83.8% and 82.0%, respectively), air temperature suddenly decreases (81.5%), and in fog or rain conditions (81.2%). In our research, alongside simple meteorological variables, we established that complex weather variables such as atmospheric fronts, in particular, the cold ones and winter anticyclonic conditions, greatly intensify the rheumatic pain, whereas summer anticyclonic conditions usually lead to a decrease in pain severity. In terms of relationships between pain intensity and weather conditions, we found the strongest correlations (ranging between 0.725 and 0.830) when temperature, relative humidity, and cloudiness are constantly high.

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