scholarly journals ROAD TEMPERATURE MODELLING WITHOUT IN-SITU SENSORS

2017 ◽  
Vol 12 (4) ◽  
pp. 241-247 ◽  
Author(s):  
Karol Opara ◽  
Jan Zieliński
Keyword(s):  
Energy Balance ◽  
Urban Areas ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Data ◽  
Forecast Errors ◽  
The Road ◽  
In Situ Sensors ◽  
Pavement Temperature

Modelling of the pavement temperature facilitates winter road maintenance. It is used for predicting the glaze formation and for scheduling the spraying of the de-icing brine. The road weather is commonly forecasted by solving the energy balance equations. It requires setting the initial vertical profile of the pavement temperature, which is often obtained from the Road Weather Information Stations. The paper proposes the use of average air temperature from seven preceding days as a pseudo-observation of the subsurface temperature. Next, the road weather model is run with a few days offset. It first uses the recent, historical weather data and then the available forecasts. This approach exploits the fact that the energy balance models tend to “forget” their initial conditions and converge to the baseline solution. The experimental verification was conducted using the Model of the Environment and Temperature of Roads and the data from a road weather station in Warsaw over a period of two years. The additional forecast error introduced by the proposed pseudo-observational initialization averages 1.2 °C in the first prediction hour and then decreases in time. The paper also discusses the use of Digital Surface Models to take into account the shading effects, which are an essential source of forecast errors in urban areas. Limiting the use of in-situ sensors opens a perspective for an economical, largescale implementation of road meteorological models.

Experimentally Measured Boundary and Initial Conditions for Simulations

2014 ◽  
Vol 1041 ◽  
pp. 293-296 ◽  
Author(s):  
Dušan Katunský ◽  
Marek Zozulák ◽  
Marián Vertaľ ◽  
Jozef Šimiček
Keyword(s):  
Boundary Conditions ◽  
Indoor Environment ◽  
Initial Conditions ◽  
Weather Data ◽  
Initial Condition ◽  
Dynamic Boundary Conditions ◽  
Climate Monitoring ◽  
Set Up ◽  
Water Profile

Real dynamic boundary conditions and initial condition has to be taken into an account when simulations need to be done. The most helpful are in situ measurement facilities with climate monitoring. Indoor environment operation modes with different air temperature and relative humidity made indoor boundary conditions. Measured weather data are used to create complete boundary conditions for the research locality. Initial condition of masonry water profile is set up. The initial and boundary conditions are considered for an individual locality simulation proposes.

scholarly journals Comparing Adjoint- and Ensemble-Sensitivity Analysis with Applications to Observation Targeting

2007 ◽  
Vol 135 (12) ◽  
pp. 4117-4134 ◽  
Author(s):  
Brian Ancell ◽  
Gregory J. Hakim
Keyword(s):  
Variance Reduction ◽  
Initial Conditions ◽  
Sampling Error ◽  
Forecast Error ◽  
Single Point ◽  
Error Variance ◽  
Initial Time ◽  
Forecast Errors ◽  
Adjoint Sensitivity ◽  
Time Optimal

Abstract The sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors. Ensemble sensitivity is defined here by linear regression of analysis errors onto a given forecast metric. It is shown that ensemble sensitivity is proportional to the projection of the analysis-error covariance onto the adjoint-sensitivity field. Furthermore, the ensemble-sensitivity approach proposed here involves a small calculation that is easy to implement. Ensemble- and adjoint-based sensitivity fields are compared for a representative wintertime flow pattern near the west coast of North America for a 90-member ensemble of independent initial conditions derived from an ensemble Kalman filter. The forecast metric is taken for simplicity to be the 24-h forecast of sea level pressure at a single point in western Washington State. Results show that adjoint and ensemble sensitivities are very different in terms of location, scale, and magnitude. Adjoint-sensitivity fields reveal mesoscale lower-tropospheric structures that tilt strongly upshear, whereas ensemble-sensitivity fields emphasize synoptic-scale features that tilt modestly throughout the troposphere and are associated with significant weather features at the initial time. Optimal locations for targeting can easily be determined from ensemble sensitivity, and results indicate that the primary targeting locations are located away from regions of greatest adjoint and ensemble sensitivity. It is shown that this method of targeting is similar to previous ensemble-based methods that estimate forecast-error variance reduction, but easily allows for the application of statistical confidence measures to deal with sampling error.

Initial Condition Sensitivity of Western Pacific Extratropical Transitions Determined Using Ensemble-Based Sensitivity Analysis

2009 ◽  
Vol 137 (10) ◽  
pp. 3388-3406 ◽  
Author(s):  
Ryan D. Torn ◽  
Gregory J. Hakim
Keyword(s):  
Sensitivity Analysis ◽  
Tropical Cyclone ◽  
Initial Conditions ◽  
Forecast Error ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecast Errors ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
The Relationship

Abstract An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

scholarly journals Evaluation of real time and future global monitoring and forecasting systems at Mercator Océan

2012 ◽  
Vol 9 (2) ◽  
pp. 1123-1185 ◽  
Author(s):  
J.-M. Lellouche ◽  
O. Le Galloudec ◽  
M. Drévillon ◽  
C. Régnier ◽  
E. Greiner ◽  
...  
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Global Analysis ◽  
Forecast Error ◽  
Systems Design ◽  
Altimeter Data ◽  
Localization Algorithm ◽  
Ocean Forecasting ◽  
Temperature Impacts

Abstract. Since December 2010, the global analysis and forecast of the MyOcean system consists in the Mercator Océan NEMO global 1/4° configuration with a 1/12° "zoom" over the Atlantic and Mediterranean Sea. The zoom open boundaries come from the global 1/4° at 20° S and 80° N. The data assimilation uses a reduced order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptative error and a localization algorithm. A 3D-Var scheme corrects for the slowly evolving large-scale biases in temperature and salinity. Altimeter data, satellite temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for the numerical ocean forecasting. This paper gives a description of the recent systems. The validation procedure is introduced and applied to the current and future systems. This paper shows how the validation impacts on the quality of the systems. It is shown how quality check (in situ, drifters) and data source (satellite temperature) impacts as much as the systems design (model physics and assimilation parameters). The validation demonstrates the accuracy of the MyOcean global products. Their quality is stable in time. The future systems under development still suffer from a drift. This could only be detected with a 5 yr hindcast of the systems. This emphasizes the need for continuous research efforts in the process of building future versions of MyOcean2 forecasting capacities.

scholarly journals Potential Predictability during a Madden–Julian Oscillation Event

2017 ◽  
Vol 30 (14) ◽  
pp. 5345-5360 ◽  
Author(s):  
Charles Jones ◽  
Jimy Dudhia
Keyword(s):  
Initial Conditions ◽  
Spatial Scales ◽  
Forecast Error ◽  
Winter Season ◽  
Wrf Model ◽  
Forecast Errors ◽  
Madden Julian Oscillation ◽  
Potential Predictability ◽  
The Tropics ◽  
Mean Square Errors

The Madden–Julian oscillation (MJO) is an important source of predictability. The boreal 2004/05 winter is used as a case study to conduct predictability experiments with the Weather Research and Forecasting (WRF) Model. That winter season was characterized by an MJO event, weak El Niño, strong North Atlantic Oscillation, and extremely wet conditions over the contiguous United States (CONUS). The issues investigated are as follows: 1) growth of forecast errors in the tropics relative to the extratropics, 2) propagation of forecast errors from the tropics to the extratropics, 3) forecast error growth on spatial scales associated with MJO and non-MJO variability, and 4) the relative importance of MJO and non-MJO tropical variability on predictability of precipitation over CONUS. Root-mean-square errors in forecasts of normalized eddy kinetic energy (NEKE) (200 hPa) show that errors in initial conditions in the tropics grow faster than in the extratropics. Potential predictability extends out to about 4 days in the tropics and 9 days in the extratropics. Forecast errors in the tropics quickly propagate to the extratropics, as demonstrated by experiments in which initial conditions are only perturbed in the tropics. Forecast errors in NEKE (200 hPa) on scales related to the MJO grow slower than in non-MJO variability over localized areas in the tropics and short lead times. Potential predictability of precipitation extends to 1–5 days over most of CONUS but to longer leads (7–12 days) over regions with orographic precipitation in California. Errors in initial conditions on small scales relative to the MJO quickly grow, propagate to the extratropics, and degrade forecast skill of precipitation.

scholarly journals Growth of Forecast Errors from Covariances Modeled by 4DVAR and ETKF Methods

2011 ◽  
Vol 139 (5) ◽  
pp. 1505-1518 ◽  
Author(s):  
Chiara Piccolo
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Forecast Errors ◽  
Model Errors ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Numerical Weather Forecasting ◽  
Ensemble Transform Kalman Filter

Numerical weather forecasting errors grow with time. Error growth results from the amplification of small perturbations due to atmospheric instability or from model deficiencies during model integration. In current NWP systems, the dimension of the forecast error covariance matrices is far too large for these matrices to be represented explicitly. They must be approximated. This paper focuses on comparing the growth of forecast error from covariances modeled by the Met Office operational four-dimensional variational data assimilation (4DVAR) and ensemble transform Kalman filter (ETKF) methods over a period of 24 h. The growth of forecast errors implied by 4DVAR is estimated by drawing a random sample of initial conditions from a Gaussian distribution with the standard deviations given by the background error covariance matrix and then evolving the sample forward in time using linearized dynamics. The growth of the forecast error modeled by the ETKF is estimated by propagating the full nonlinear model in time starting from initial conditions generated by an ETKF. This method includes model errors in two ways: by using an inflation factor and by adding model perturbations through a stochastic physics scheme. Finally, these results are compared with a benchmark of the climatological error. The forecast error predicted by the implicit evolution of 4DVAR does not grow, regardless of the dataset used to generate the static background error covariance statistics. The forecast error predicted by the ETKF grows more rapidly because the ETKF selects balanced initial perturbations, which project onto rapidly growing modes. Finally, in both cases it is not possible to disentangle the contribution of the initial condition error from the model error.

scholarly journals Feasibility of Vertical Rainwater Harvesting via In-situ Measurement of Wind-driven Rain Loads on Building Facades in a Tropical Climate

2021 ◽  
Vol 8 (3) ◽  
pp. 27-45
Author(s):  
Mozhgan Samzadeh ◽  
Nazli Che Din ◽  
Zunaibi Abdullah ◽  
Norhayati Mahyuddin ◽  
Muhammad Azzam Ismail
Keyword(s):  
Urban Areas ◽  
Rainwater Harvesting ◽  
Tall Buildings ◽  
Weather Data ◽  
Surface Areas ◽  
In Situ Data ◽  
Building Facade ◽  
Building Facades ◽  
Monsoon Winds

Rainwater is an alternative water resource to fulfill sustainable management of freshwater particularly in the regions receive abundant annual amounts of precipitation such as tropical Malaysia. To collect and store rainwater, rainwater harvesting system has been practiced since ancient from horizontal surfaces mostly rooftop of buildings in urban areas. Nowadays, this method in modern urban areas with tall buildings is considered inadequate and uneconomical because the ratio of facade surface areas is much higher than the ratio of roof surface areas. On the other hand, all rain has a horizontal velocity due to wind acting upon rain droplets which is called wind-driven rain (WDR). Growing tall buildings and the presence of WDR phenomenon make building façade surfaces the available promising surfaces to harvest substantial rainwater vertically and more efficiently. This article presents a one-year field measurement results that aims at quantifying the WDR loads impinged on the vertical facade areas of a pilot building located at the main campus of the University Malaya in Kuala Lumpur, Malaysia. Detailed descriptions of the gauge design, building, the measurements of on-site WDR, rainfall duration time, and weather data are presented. Records show that monsoon winds characteristics have significant influence on the WDR loads on the building facades compare to horizontal rainfall intensity. Finally, the collected in-situ data are exploited to validate data and determine WDR coefficient (γ) to estimate the amount of WDR on a building façade via an empirical WDR relationship. Results show the feasibility of each square meter of vertical façade area to supply 12% of non-potable or 4.9% of potable water-usage per capita per day.

The Variability and Predictability of Axisymmetric Hurricanes in Statistical Equilibrium

2013 ◽  
Vol 70 (4) ◽  
pp. 993-1005 ◽  
Author(s):  
Gregory J. Hakim
Keyword(s):  
Environmental Variability ◽  
Initial Conditions ◽  
Forecast Error ◽  
Statistical Equilibrium ◽  
Intensity Change ◽  
Order Estimate ◽  
Forecast Errors ◽  
Initial Structure ◽  
Axisymmetric Solution ◽  
Maximum Wind

Abstract The variability and predictability of axisymmetric hurricanes are determined from a 500-day numerical simulation of a tropical cyclone in statistical equilibrium. By design, the solution is independent of the initial conditions and environmental variability, which isolates the “intrinsic” axisymmetric hurricane variability. Variability near the radius of maximum wind is dominated by two patterns: one associated primarily with radial shifts of the maximum wind, and one primarily with intensity change at the time-mean radius of maximum wind. These patterns are linked to convective bands that originate more than 100 km from the storm center and propagate inward. Bands approaching the storm produce eyewall replacement cycles, with an increase in storm intensity as the secondary eyewall contracts radially inward. A dominant time period of 4–8 days is found for the convective bands, which is hypothesized to be determined by the time scale over which subsidence from previous bands suppresses convection; a leading-order estimate based on the ratio of the Rossby radius to band speed fits the hypothesis. Predictability limits for the idealized axisymmetric solution are estimated from linear inverse modeling and analog forecasts, which yield consistent results showing a limit for the azimuthal wind of approximately 3 days. The optimal initial structure that excites the leading pattern of 24-h forecast-error variance has largest azimuthal wind in the midtroposphere outside the storm and a secondary maximum just outside the radius of maximum wind. Forecast errors grow by a factor of 24 near the radius of maximum wind.

scholarly journals Tropical cyclones prediction by numerical models in India Meteorological Department

MAUSAM ◽  
2021 ◽  
Vol 57 (1) ◽  
pp. 47-60
Author(s):  
Y. V. RAMA RAO ◽  
H. R. HATWAR ◽  
GEETA AGNIHOTRI
Keyword(s):  
Initial Conditions ◽  
Numerical Models ◽  
Forecast Error ◽  
India Meteorological Department ◽  
Lagrangian Model ◽  
Limited Area ◽  
Track Forecast ◽  
Forecast Errors ◽  
Error Statistics ◽  
The Impact

lkj & bl 'kks/k&Ik= esa Hkkjr ekSle foKku foHkkx ¼Hkk- ekS- fo- fo-½ esa viukbZ xbZ pØokr izfr:fir djus dh dfYir rduhdksa ij ppkZ dh xbZ gSA vDrwcj 1999 esa mM+hlk esa vk, egkpØokr ds izkjfEHkd {ks=ksa esa dkYifud Hkzfeyrk dk mi;ksx djds] pØokr ds fof’k"V ekWMy] Doklh ySaxjfx;u ekWMy ¼D;w- ,y- ,e-½ ls 72 ?kaVs ds iwokZuqeku vkSj Hkkjr ekSle foKku foHkkx ds lhfer {ks= fun’kZ ¼,y- ,- ,e-½ ls 36 ?kaVs ds iwokZuqeku izfr:fir fd, x,A bl 'kks/k esa] 26 ls 28 vDrwcj rd dh izkjafHkd fLFkfr;ksa ds vk/kkj ij D;w- ,y- ,e- ls pØokr ds ekxZ ds iwokZuqeku dh vkSlr =qfV;k¡ 24 ?kaVs ds fy, 21 fd-eh-] 48 ?kaVs ds fy,  91 fd-eh- vkSj 72 ?kaVs ds fy, 179 fd-eh- jghA 1998&2004 rd ds fiNys lkr o"kksZa ds nkSjku D;w- ,y- ,e- ls pØokr ds ekxZ ds iwokZuqeku dh =qfV;ksa ds vk¡dM+ksa ij Hkh blesa ppkZ dh xbZ gSA blds vykok] ,y- ,- ,e- ls fd, x, iwokZuqeku ij izkjafHkd fLFkfr;ksa ds izHkko dh Hkh tk¡p dh xbZA fofHkUu izkjafHkd fLFkfr;ksa ls rS;kj fd, x, vkSlr ¼lesfdr½ iwokZuqeku ls 24 ?kaVs ds iwokZuqeku esa 123 fd-eh- vkSj 36 ?kaVs ds iwokZuqeku esa 81 fd-eh- dh =qfV;k¡ ikbZ xbZ] tks ,dek= iwokZuqeku dh rqyuk esa de jghA bu iz;ksxksa ls ;g irk pyk fd dkYifud Hkzfeyrk okys D;w- ,y- ,e- ekWMy ls pØokr ds ekxZ  dk lVhd iwokZuqeku izkIr fd;k tk ldrk gS tks vHkh rd la[;kRed ekWMyksa ls miyC/k gks ikrk FkkA  In the present paper, the cyclone bogusing techniques followed in India Meteorological Department (IMD) were discussed. Using the idealized vortex in the initial fields for Orissa super cyclone October 1999, the specialized cyclone model, Quasi-Lagrangian Model (QLM) 72 hours track forecast and also 36 hours forecast with IMD limited area model (LAM) were simulated. In this case, the QLM average track forecast errors based on 26-28 October initial conditions were 21 km for 24 hours, 91 km for 48 hours and 179 km for 72 hours. Also the QLM track forecast error statistics during the last 7 years 1998-2004 are discussed. In addition, the impact of initial conditions on the LAM forecast was examined. It was observed that the mean (ensemble) forecast generated from different initial conditions was shown track error of 123 km in 24 hours and 81 km in 36 hours forecast which is less than individual forecast. These experiments have established that the QLM model, with idealized vortex, provides track forecast within an accuracy level that was currently available from numerical models.  

Assessing Predictability with a Local Ensemble Kalman Filter

2007 ◽  
Vol 64 (4) ◽  
pp. 1116-1140 ◽  
Author(s):  
David Kuhl ◽  
Istvan Szunyogh ◽  
Eric J. Kostelich ◽  
Gyorgyi Gyarmati ◽  
D. J. Patil ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Data Assimilation Scheme ◽  
Ensemble Mean ◽  
Assimilation Scheme

Abstract In this paper, the spatiotemporally changing nature of predictability is studied in a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), a state-of-the-art numerical weather prediction model. Atmospheric predictability is assessed in the perfect model scenario for which forecast uncertainties are entirely due to uncertainties in the estimates of the initial states. Uncertain initial conditions (analyses) are obtained by assimilating simulated noisy vertical soundings of the “true” atmospheric states with the local ensemble Kalman filter (LEKF) data assimilation scheme. This data assimilation scheme provides an ensemble of initial conditions. The ensemble mean defines the initial condition of 5-day deterministic model forecasts, while the time-evolved members of the ensemble provide an estimate of the evolving forecast uncertainties. The observations are randomly distributed in space to ensure that the geographical distribution of the analysis and forecast errors reflect predictability limits due to the model dynamics and are not affected by inhomogeneities of the observational coverage. Analysis and forecast error statistics are calculated for the deterministic forecasts. It is found that short-term forecast errors tend to grow exponentially in the extratropics and linearly in the Tropics. The behavior of the ensemble is explained by using the ensemble dimension (E dimension), a spatiotemporally evolving measure of the evenness of the distribution of the variance between the principal components of the ensemble-based forecast error covariance matrix. It is shown that in the extratropics the largest forecast errors occur for the smallest E dimensions. Since a low value of the E dimension guarantees that the ensemble can capture a large portion of the forecast error, the larger the forecast error the more certain that the ensemble can fully capture the forecast error. In particular, in regions of low E dimension, ensemble averaging is an efficient error filter and the ensemble spread provides an accurate prediction of the upper bound of the error in the ensemble-mean forecast.

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