Validation of the Proposed Texas Mesonet from the aspect of site spacing density. Ibrahim SONMEZ

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1 Validation of the Proposed Texas Mesonet from the aspect of site spacing density. Ibrahim SONMEZ Ph.D Canditate Texas Tech University Atmospheric Science Group

2 Overview Observation System over Texas Proposed Texas Mesonet Project Literature Review Observational Error Estimation Spatial correlation analysis Power Spectrum Analysis Error estimation in true Fourier coefficients Conclusion & Suggestions

3 NWS Sites:

4 Coop Sites:

5 West Texas Mesonet sites

6 The Others:

7 What is wrong with the current network? Not every parameter is observed in every site Time resolution The available surface observations are few and far away Surface site spatial resolution: km Upper air site spatial resolution: km Only 1 of every 5 county is monitored Difficult to detect mesoscale phenomena Very poor data from Gulf of Mexico

8 Literature Review: 1. Statistical Approach: Presented by Gandin(1963) and refined by Huss(1971), Theibaux(1973,1975) and Schlatter(1975) Principle: To minimize the interpolation error at grid points Requirement: Statistical structure (time & space covariance function) Assumption: Domain is homogenous and isotrop Aplications: Upper air network expantion by Gandin et. al.(1967), and Gandin(1970)

9 Literature Review: 2. Entropy Approach: Based on Shanon (1949) information theory Entropy is defined as a measure of uncertainty associated with the probability of occurrence of an event Aplications:Hydrological network design Caselton and Husain (1980), optimum air monitoring network design Husain and Khan (1983), meteorological network expansion (Husain and Ukayli 1983; Husain et al. 1984; Husain et al. 1986)

10 Literature Review: 3. Dynamical Approach: Numerical models are used to determine the observational density due to the error growth rate of the model Limitations: evaluates the whole network, effected by the network configuration & time of the run Aplications: Alaka and Lewis, (1967,1968), Kasahara (1972), Kasahara and Williamson (1972)

11 Proposed Texas Mesonet sites

12 Expected benefits of Mesonet: Weather information: Improvement in the performance of nowcasting and forecasting Energy: Saving in energy use & exploring new energy sources such as, wind and solar energy Air Quality: Provide better input for models & reduce medical costs Agriculture: Recommendations about planting, watering and harvesting Forest & Grassland fire management: Determination of the accurate fire weather conditions Water Management: Accurate determination of rainfall, flood control & power use Education: Opportunity for using a scientific data & research

stations Data Period Total period Pressure 14 1970-1994 25

13 Analysis over Texas Dataset: Parameters: Pressure, Temperature, Rel. Humidity, Wind Observations: 3 hourly Parameters Number of stations Data Period Total period Pressure Temperature R. Humidity Wind

14 List of the stations STATION NAME STATION WBAN # LATITUDE LONGITUDE ELEVATION (M) DALLAS/FT WORTH AP 3927 N32:54 W097: VICTORIA REGIONAL AP N28:51 W096: PORT ARTHUR JEFFERSN N29:57 W094: BROWNSVILLE INTL AP N25:54 W097: SAN ANTONIO INTL AP N29:32 W098: CORPUS CHRISTI INTL N27:46 W097: HOUSTON INT'CNTNL AP N29:58 W095: AUSTIN MUNICIPAL AP N30:17 W097: WACO MADISN COOPRAP N31:37 W097: ABILENE MUNI AP N32:25 W099: WICHITA FALLS MUN AP N33:58 W098: MIDLAND REGIONAL TER N31:57 W102: SAN ANGELO MATHIS FD N31:22 W100: LUBBOCK REGIONAL AP N33:39 W101: AMARILLO INTL ARPT N35:14 W101:

15 Site locations over Texas

16 Covariance (F**2) Observational Error Estimation Assumptions: Errors are symmetric (the average is zero). Errors are not intercorrelated. Errors are not correlated with the true values of the quantity Cov ~ (f,f)=cov(f,f)+σ 2 E σ 2 E =Cov ~ (f,f)-cov(f,f) (Gandin, 1969) HOUSTON (TEMPERATURE, F) y = 1E-08x 3-4E-05x x Distance (km)

17 cavariance (F**2) TEXAS TEMPERATURE y = 6E-08x 3-9E-05x x R 2 = distance (km) Variance (at x=0) Intercepting point Difference Parameter Average variance 95 % Confid. interval Average intercepting 95 % Confid. interval Error Variance Pressure (mb^2) ± ± Temperature (C^2) ± ± Relative Humidity ± ± Wind_u (m/s^2) ± ± Wind_v (m/s^2) ± ±

18 Spatial Correlation Analysis r 1,2 = N 1 i= 1 N å ( x 1i - x s x 1 1 ) ( x s x 2 2i - x 2 ) Thiebaux,1974 Candidate analytic correlation functions. Equation Form Fixed Parameter F1 g a Cos( wx) exp( - lx ) none F2 g a Cos( wx) exp( - lx ) g =2.0 F3 g a exp( - lx ) none

19 Parameter analysis Par. Eq. α ω λ γ AES F E E F E E F E F E E F E E F E F E E F E E F E F E E F E E F E F E E F E E F E Press. Temp. Humid. Wind_U Wind_V

20 correlation coeff. correlation coeff. correlation coeff. correlation coeff. correlation coeff. Spatial Correlation Scatter & Functions Pressure Temperature Humidity y=0.99*exp(-1.02e-06x^1.9) distance (km) y=0.98*cos(8.98e-04x)*exp(-3.33e-04x^1.1) distance (km) 1 y=0.99*exp(-1.05e-03x^1.1) distance (km) Wind_U y=0.73*cos(1.44e-03x)*exp(-1.57e-03x^1.0) distance (km) Wind_V y=0.87*cos(1.70e-03x)*exp(-1.24e-04x^1.3) distance (km)

21 Power Spectrum ï î ï í ì = ò - - function ariance Auto u C Spectrum Power m S number Wave m du e u C m S T T T m u i cov : ) ( : ) ( : where, ) ( ) ( 2p 2 ) ( ) ( s r u C u = Spectral density function: ò - - = T T T m u i du e u m S p r s 2 2 ) ( ) (

22 Pow er density Pow er density Pow er density Pow er density Pow er density Power Spectrum of parameters: Pressure w ave number (m) Temperature w ave number (m) Humidity wave number (m) Wind_U w ave number (m) Wind_V w ave number (m)

23 Cumulative power spect. Cumulative Power Spectrum wave number (m) P T H W_U W_V

24 Error estimation in true Fourier coefficients - to - Assumption: True field stretching from 2 2 x i2pm ìy( x) : True field Y = å L ( x) ane í - îan : True( coplex) coefficients L L a n = L x i2pn L L ò Y ( x) e L - 2 dx However, observation are taken at grid spacing of Dx where Dx = L N

25 Error estimation in true Fourier coefficients 1 (x ) 1 ˆ 2 N-1 0 j 2 N-1 0 j j x e M L x e L a L x n i j L x n i n j j D + D Y = - = - = å å p p î í ì error t Measuremen : M a of Estimation : â where j n n m m m a a ˆ - = e [ ] N ) (.. ˆ M m N m N m m m m S S a a s e e + + = - = - +

26 Error square/sm Error Square term variation with wave # Temperature km 150 km 100 km 75 km 50 km Wave number (m)

27 Error square/sm Error square/sm Error square/sm Errror squre/sm Error Square term variation with wave # Pressure Humidity km 150 km 100 km 75 km 50 km km 150 km 100 km 75 km 50 km Wave number (m) Wave number (m) Wind_U Wind_V km 150 km 100 km 75 km 50 km km 150 km 100 km 75 km 50 km Wave number (m) Wave number (m)

28 Critical Wave numbers for parameters Spacing Δ x(km) Pressure Temperature Humidity Wind_U Wind_V

29 Sm, e**2 True field error variance estimation Sm error square Sm =1 e**2 0 0 k m

30 Error variance Error variance(m/s^2) Error variance (m/s^2) Error variance(mb^2) Error variance (C^2) True field error variance variation Pressure Temperature Site spacing (km) Site spacing (km) Relative Humidity Wind_U Wind_V Site Spacing (km) Site spacing (km) Site spacing (km)

31 True field error variance decrement Parameter Error variance at 200 km spacing Error variance at 50 km spacing Decrement in error variance (%) Pressure (mb^2) Temperature (C^2) Relative Humidity Wind_u (m/s^2) Wind_v (m/s^2) Parameter Error variance at 200 km spacing Error variance at 50 km spacing Decrement in error variance (%) Pressure (mb^2) Temperature (C^2) Relative Humidity Wind_u (m/s^2) Wind_v (m/s^2)

and v component of the wind Error square term is very sensitive to site spacing amounts Almost a linear decreasing trend in error

32 Conclusions & Suggestions Large scale variations are governing most of the parameter variation Large scale variation was highest in the pressure, temperature and humidity Small scale variations are relatively important in the u component of the wind, humidity and v component of the wind Error square term is very sensitive to site spacing amounts Almost a linear decreasing trend in error variance is observed by smaller spacing amounts % decrement in error variance is observed between 200 and 50 km spacing

33 Conclusions & Suggestions Useful curves are obtained to identify the site spacing amount depending on the desired error variance or to identify the error variance depending on the desired site spacing amount Financial aspect of the problem also has to be considered Same analysis may be repeated by considering the East- West & North-South variation of the spatial correlation Some other Agricultural parameter might be interesting to analyze in the same sense

34 Acknowledgments Dr. Tim Doggett (advisor) Dr. John Nielsen-Gammon (ex advisor) Dr. Gerald North (Dept. Head) Grad students in Atm. Science Group Others.

Texas Statewide Mesonet Network (TexMesonet) Dr. Kantave Greene 11/15/17

Texas Statewide Mesonet Network (TexMesonet) Dr. Kantave Greene 11/15/17 1 Observe weather features Why Mesonets? Thunderstorms Squall lines Dry-lines Sea breezes Improve weather & flood forecasts Drought