Research on Unmanned Surface Vessel for Meteorological Detection

Wu, Changwen; Li, Baokui; Fei, Qing; Huang, Xiaosong; Fang, Xuyang

doi:10.23919/ccc52363.2021.9549595

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…Then, the calculation is performed based on the cloud cover diagnostic algorithm proposed by J. Wu et al. (2012):

{C}_{k}=0.2\beta +0.8{C}_{k}^{\prime }

\beta =\left\{\begin{array}{@{}l@{}}0\qquad {w}_{k}\le 0\\ \frac{{w}_{k}}{\overline{w}}\,0< {w}_{k}< \overline{w}\\ 1\qquad {w}_{k}\ge \overline{w}\end{array}\right.,{{C}_{k}}^{\prime }={\left(\frac{{\text{RH}}_{k}-{\text{RH}}_{c,k}}{1-{\text{RH}}_{c,k}}\right)}^{2}

where C k and

{C}_{k}^{\prime }

are the final and initial guess cloud cover in the k th layer, respectively. β represents the effect of upward motion on cloud formation, RH k represents relative humidity in the k th layer, RH c , k denotes the threshold value of relative humidity in the k th layer, w k is the vertical velocity at the k th layer at this grid point, and

\overline{w}

is the average value of vertical velocities of all layers at this grid point.…”

Section: Observations and Da Systemmentioning

confidence: 99%

Impact of a New Bias Correction Predictor for FY‐4A AGRI All‐Sky Data Assimilation on Typhoon Forecast

Shi,

Yang,

Min

et al. 2023

JGR Atmospheres

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The all‐sky assimilation module for Advanced Geostationary Radiation Imager (AGRI) onboard the Chinese new generation of geostationary meteorological satellites Fengyun‐4A (FY‐4A) is constructed for the first time in Weather Research and Forecasting Model Data Assimilation (WRFDA) model with Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV). Based on the characteristics of bias distribution, the cloud effect average is selected as a new bias correction (BC) predictor to remove cloud‐related biases in all‐sky conditions. The impact of the modified BC scheme and AGRI all‐sky assimilation on typhoon Lekima (2019) forecast is evaluated through a set of cycle assimilation experiments with WRFDA 3DVAR component. The result shows that, compared with other BC schemes, the modified BC scheme can effectively reduce the mean standard deviation, root mean square error, and absolute mean of observation departure in all‐sky conditions. Meanwhile, the AGRI all‐sky assimilation with the modified BC scheme obtains significant improvements in track prediction. Substantial error reductions in model variables, such as wind, temperature, and humidity, are also produced.

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“…Then, the calculation is performed based on the cloud cover diagnostic algorithm proposed by J. Wu et al. (2012):