Sensitivity of the predictor to meteorological variables.

<p>Bars indicate changes as described for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119082#pone.0119082.g004" target="_blank">Fig. 4</a>. P, daily average air pressure; Ta, average air temperature; WS, wind speed; SD, sunshine duration; R, total radiation.</p

Correlation coefficients of Chlorophyll-a (Chl-<i>a</i>) with water quality and Chl-<i>a</i> with meteorological variables.

<p>Note:</p><p>* Tw, water temperature; Cond, conductivity; Tran, transparency; CL, chloride; Hard, hardness; NH₄-N, ammonia nitrogen; NO₃-N, nitrate-nitrogen; NO₂-N, nitrite-nitrogen; TN, total nitrogen; DO, dissolved oxygen; PI, permanganate index; BOD, biochemical oxygen demand; TP, total phosphorus; PS, phosphate; TS, total solids; SPS, suspended solids; SLS, soluble solids; SAL, salinity; P, daily mean air pressure; Pmax, maximum air pressure; Pmin, minimum air pressure; Ta, average air temperature; Tamax, maximum air temperature; Tamin, minimum air temperature; PCP, precipitation; WS, average wind speed; WSmax, maximum wind speed; SD, sunshine duration; R, total radiation.</p><p>** Day₀, data of the predicted day;</p><p>*** Day₁₅, Day₃₀…Day₁₆₅, the average data of the previous 15, 30…165 days.</p><p>Correlation coefficients of Chlorophyll-a (Chl-<i>a</i>) with water quality and Chl-<i>a</i> with meteorological variables.</p

Sensitivity of the predictor to water quality variables.

<p>Bars indicate changes in Chl-<i>a</i> values caused by changes in the input variables, which were altered by 5%, 10% and 20%. Black, slash-filled, and cross line-filled bars indicate the change in Chl-<i>a</i> values caused by 5%, 10%, and 20% changes in input variables, respectively. Tw, water temperature; DO, dissolved oxygen; PI, permanganate index; TP, total phosphorus; NO₃-N, nitrate-nitrogen.</p

Features and variables of the Chl-<i>a</i> prediction model.

<p>Note:</p><p>* Features are the same as listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119082#pone.0119082.t001" target="_blank">Table 1</a>.</p><p>** Average value of the variables over the indicated number of preceding days. For example, Tw₃₀ represents the average water temperature of the preceding 30 days.</p><p>Features and variables of the Chl-<i>a</i> prediction model.</p

Average Corr and CSI of all 8 events for 0–120 min predictions vs. different spatial resolutions of FY-2F using PPLK.

<p>Average Corr and CSI of all 8 events for 0–120 min predictions vs. different spatial resolutions of FY-2F using PPLK.</p

Average measures of PPLK, PMC, PHS and other short-term quantitative precipitation nowcasting (QPN) methods with 30 min, 60 min, 90 min and 120 min lead time.

<p>*PPLK: Pixel-based QPN using the Pyramid Lucas-Kanade Optical-Flow method; MCM: Pixel-based QPN using the maximum correlation method; PHS: Pixel-based QPN using Horn-Schunck optical-flow method; PERCAST: PERsiann (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) - -ForeCAST; PERCAST-GD: PERsiann-ForeCAST considering the storm Growth and Decay (GD) whose area increases or decreases compared to previous moments; WDSS: Warning Decision Support System; PER: PERsistence. The PMC, PHS and PPLK were compared based on 2338 images of 8 periods of FY-2F in this paper. The PERCAST-GD, PERCAST, WDSS~II and PER was compared by Zahraei et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140044#pone.0140044.ref006" target="_blank">6</a>] based on four storms from GOES-IR data over a rectangular region in 80–115°W and 32–45°N.</p><p>Average measures of PPLK, PMC, PHS and other short-term quantitative precipitation nowcasting (QPN) methods with 30 min, 60 min, 90 min and 120 min lead time.</p

Average 6 measures of QPN with 30 min, 60 min, 90 min and 120 min lead time for 8 periods using the PPLK: coefficient of correlation (Corr), normalized mean square error (NMSE), probability of detection (POD), false-alarm rate (FAR), and critical success index (CSI).

<p>Average 6 measures of QPN with 30 min, 60 min, 90 min and 120 min lead time for 8 periods using the PPLK: coefficient of correlation (Corr), normalized mean square error (NMSE), probability of detection (POD), false-alarm rate (FAR), and critical success index (CSI).</p

Information for the rainfall images of 8 periods in 2013 using Fengyun-2F (FY-2F), which include the time, length, spatial coverage, and main cloud types of the precipitation systems.

<p>* mmddhhmm: Month day hour minute.</p><p>** The scale definition in this study, such as large, medium and small scales, is not strictly based on meteorology. It is a relative concept that considers the spatial distribution of cloud on a geostationary satellite.</p><p>Information for the rainfall images of 8 periods in 2013 using Fengyun-2F (FY-2F), which include the time, length, spatial coverage, and main cloud types of the precipitation systems.</p

Coefficient of correlation (Corr) and critical success index (CSI) of QPN using Pyramid Lucas-Kanade Optical Flow method (PPLK) vs. 30 min, 60 min, 90 min and 120 min lead time.

<p>for 2338 images of 8 periods using Pixel-based.</p

Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN) from a Geostationary Satellite

<div><p>The prediction of the short-term quantitative precipitation nowcasting (QPN) from consecutive gestational satellite images has important implications for hydro-meteorological modeling and forecasting. However, the systematic analysis of the predictability of QPN is limited. The objective of this study is to evaluate effects of the forecasting model, precipitation character, and satellite resolution on the predictability of QPN usingimages of a Chinese geostationary meteorological satellite Fengyun-2F (FY-2F) which covered all intensive observation since its launch despite of only a total of approximately 10 days. In the first step, three methods were compared to evaluate the performance of the QPN methods: a pixel-based QPN using the maximum correlation method (PMC); the Horn-Schunck optical-flow scheme (PHS); and the Pyramid Lucas-Kanade Optical Flow method (PPLK), which is newly proposed here. Subsequently, the effect of the precipitation systems was indicated by 2338 imageries of 8 precipitation periods. Then, the resolution dependence was demonstrated by analyzing the QPN with six spatial resolutions (0.1atial, 0.3a, 0.4atial rand 0.6). The results show that the PPLK improves the predictability of QPN with better performance than the other comparison methods. The predictability of the QPN is significantly determined by the precipitation system, and a coarse spatial resolution of the satellite reduces the predictability of QPN.</p></div