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Jun-ichi Furumoto, Toshitaka Tsuda

Measurements of Temperature and Humidity Profiles with a Wind-Profiling Radar and RASS Observations. Jun-ichi Furumoto, Toshitaka Tsuda Research Institute for Sustainable Humanosphere, Kyoto University. Introduction.

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Jun-ichi Furumoto, Toshitaka Tsuda

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  1. Measurements of Temperature and Humidity Profileswith a Wind-Profiling Radar and RASS Observations. Jun-ichi Furumoto, Toshitaka Tsuda Research Institute for Sustainable Humanosphere, Kyoto University

  2. Introduction • Continuous monitoring of wind velocity, temperature and humidity profiles with good temporal and height is very important for the research on the thermodynamic mechanism of and the prediction of severe phenomena. • A wind profiling radar enables us to observe the three components of wind velocity with good temporal and height resolution. • 1. Radio Acoustic Sounding System (RASS) • A radar remote sensing technique to observe temperature profile by combining acoustic and radio waves. • The improvement of vertical resolution by applying imaging technique to RASs measurement. • 2. Estimation of Humidity profile • From the characteristics of turbulence echo, humidity profiles can be estimated with a wind profiling radar.

  3. Basic Principle of RASS measurement Acoustic velocity cs: Tv: atmospheric virtual temperature (K) Kd=20.047 Bragg condition radar wavenumber vector acoustic wavenumber vector 5 Height(km) 0 300 270 Temperature(K) Acoustic transmission from the ground Monochromatic wave Radio transmission toward acoustic wavefront Time Acoustic velocity is calculated from the Doppler shift of RASS echo FM chirped acoustic wave FM-chirped acoustic wave is used in the RASS observation Acoustic velocity is calculated from the Doppler

  4. The MU radar – RASS RASS with the MU radar (MU radar-RASS) can provide temperature profiles from the height of 1.5 km to the lower stratosphere with temporal and height resolutions of a few minutes and 150 m, respectively. RASS technique has been applied to the other radars. Frequency-domain Interferometric Imaging (FII) technique is also applied to the MU radar-RASS measurement to improve vertical resolution of temperature profiles obtained with RASS.

  5. RASS echo with the FM chirped acoustic wave z FM chirped acoustic wave High frequency Bragg height: Height at which the Bragg condition is fully satisfied. Scanning height Height (km) Bragg area: Height range where the transmitted radio wave is backscattered. range gate temperature time Low frequency Amplitudeof received echo acoustic sweep ratio(Hz/s) • Precise shape of amplitude is determined by • radar range gate function • Temperature profile (Masuda et al. 1992) • Typ. value of Lfm • 60-80 m (46.5MHz radar) • 20-30 m (443MHz radar) tc time By extracting the partial time series from the received time series, we can suppress the scattering from the unexpected height, and improve the height resolution.

  6. Estimation Algorithm of RASS-FII imaging Received signal of RASS imaging observation Amplitude of the received signal height Band Path Filter temperature Filtered result i=0 Yes Amplitude variation correction No i>imax i=0 ? Timeseries extraction 5 15 25 Time (s) Frequency domain interferometric imaging (FII) Turbulence imaging result • Amplitude variation due to range gate function and temperature profile are compensated with • model temperature profile with a constant lapse ratio (in the initial step) • estimation result of the previous iteration step Virtual temperature calculation i=i+1 No Yes Final result

  7. The MUradar-RASS imaging observation Virtual Temperature Difference between radar and radiosonde results The MU radar-RASS imaging observation was conducted on October 29-31, 2006 Conventional observation FII result Radar parameter Half-Power Full-Width of RASS-FII simulation is 57 m. Vertical resolution +75m Black solid: RASS-FII averaged for 22.5 min. Red dot-dashed: radiosonde Green error bar: s.e. of RASS-FII for 22.5 min. Black dashed: the reference of nominal radar range gate width Acoustic parameter Range gate width (150 m) Normalized sensitivity -75m DF=17.5 Hz/s

  8. RASS-FII observation campaign with a large DF of 50 Hz/s • Observation period: November 11-14, 2008 • The expected vertical resolution of temperature profiles : 50 m. Virtual temperature fluctuation with RASS-FII Echo power intensity obtained by FII analysis November 14, 2008 Strong turbulence echo regions seem to concern with larger temperature regions shown by the circles.

  9. Basic Principle of Humidity Estimation Method. Specific humidity is derived from the relation between turbulence echo power and height derivative of refractive index. M: height derivative of refractive index h: echo power intensity N: Brunt-Väisäla frequency squared e: turbulence energy dissipation rate p: pressure q: specific humidity g: gravity acceleration K0 ,K1 K2: constant z: height • Detailed variation of N2 is estimated from the temperature profile with RASS measurements. • Turbulence energy dissipation rate is derived from the spectral width of turbulence echo. • →　q profile can be estimated from turbulence and RASS measurements. • The sign of M is unknown from radar observations. • The sign is determined to agree the integrated water vapour (IWV) with GPS result. • The 1DVAR is applied to the determination of the appropriate sign of M.

  10. Time-height variation of radar-derived q First Guess: Time interpolated 12-hourly radiosonde result. Radiosonde-derived q Radiosonde is launched every 3 hours First guess The analysis result successfully retrieved detailed humidity variations that cannot be expressed by the first guess. Analysis result

  11. Conclusions • The temperature and humidity estimation method with a wind profiling radar with Radio Acoustic Sounding System (RASS) measurement was presented. • The Frequency-domain Interferometric Imaging(FII) technique was applied to RASS measurement in an attempt to improve the vertical resolution of temperature profiles obtained with RASS measurements. • Humidity estimation method from the wind profiling radar was also presented. By applying 1DVar to determine the appropriate sign of refractive index gradient, which cannot be derived from radar measurements, the humidity profiles was successfully estimated from turbulence and RASS echo.

  12. Estimation error due to the variation of B q profiles are derived from the first guess calculated from 6-, 12-, 18-, and 24-hourly radiosonde results. Black solid：radiosonde Black dashed ：first guess Red：analysis q profiles at 6 LT on Jul. 30, 1999 In all figures analysis is in good agreement with radiosonde results. 6hr 12hr 18hr 24hr The random error profiles of analysis result show similar values in all panels, regardless of the difference of the time-interpolation interval of radiosonde results. Black：first guess Red：analysis result Blue：conventional method Random error averaged for 34 profiles These results shows 1D-Var is effective when we estimate humidity profiles using the forecast of meso-scale model. 6hr 12hr 18hr 24hr

  13. Estimation with the forecast of prediction model The forecast of the operational Meso-Scale Model (MSM) of the Japan Meteorological Agency (JMA) used as the first guess, instead of the time-interpolation of radiosonde data. The forecast error used at JMA is employed as the background error. . q profile Difference from radiosonde Bias error averaged for 6 profiles Dotted: MSM Black solid: analysis Red: radiosonde result Both bias and random errors in the analysis are smaller than these in the first guess. Random error averaged for 6 profiles 15LT Jul. 29, 2002 The discrepancy in the analysis is smaller than that in the first guess below 3.0 km.

  14. Future works • Relationship between detailed static stability and the turbulence echo structure will be analyzed to investigate the detailed turbulence echo characteristics. • We will apply the RASS-FII method to 443MHz- wind profiling radar with RASS aiming to better vertical resolution, because numerical simulation predicts the vertical resolution improves to 30 m.

  15. Radar Equation of RASS echo Received electric field of RASS echo with an FM chirped acoustic wave :range function :angular velocity amplitude Doppler shift term :upper and bottom limit of integration Range gate function

  16. Basic principles of variational method Variational method is a data assimilation technique to determine the most reasonable atmospheric state based on maximum likelihood estimation. The observation operator H, is defined to convert the atmospheric state vector to the observational one as: x: state vector consisting of the atmospheric state variables y:observation vector consisted of observed variables The analysis vector xa is determined as x when the conditional probability of x given the first guess (xb) and observation results (yo) has its maximum value. xais obtained as xto minimize the cost function J(x) as : If J(x) is differentiable, xacan be derived by minimizing J(x) using a quasi-Newton method. B:background covariance metrics R: observation covariance metrics

  17. Background and observation vector The variational method is applied to the assimilation of the MU radar-RASS observation results for the period from July 29 to August 5, 1999 By assimilating the IWVGPS together with the radar-derived |M|, the signs of |M| are constrained. p0: pressure at the lowest height. Ti: temperature at the j-th height RHi: relative Humidity at the j-th height IWVGPS: Integrated Water Vapor with GPS • The first guess of the atmospheric state vector was obtained from the time-interpolation of radiosonde observations. Each element of xbwas calculated from 6-, 12-, 18- and 24-hourly radiosonde results.

  18. Expansion of 1D-Var for humidity estimation • When the absolute value of |M|is assimilateddirectly into the background atmospheric state, J(x) has many local minima, and it is very difficult to find the global minimum using finite computer resources. • To reduce the calculation cost of the assimilation, a new cost function was formulated by considering the statistical probability (Pr(z)) of the sign of |M|. Determination of sign of |M| R-1(i,i) : the (i,i)-th component of R-1 Genetic algorithm (GA) is used to find the global minimum Pr(z) is calculated data from almost 1500 radiosondes launched since 1986. After the sign of M was determined, y0 after the previous step, is again assimilated using the general cost function. The quasi-Newton method (BFGS method) is employed for the optimization

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