prl900

import numpy as np
from osgeo import gdal
import scipy.optimize as opt
import scipy.ndimage
from PIL import Image
import netCDF4
import json
import os
import sys
import datetime

prl900

{
    "Conventions": "CF-1.6, ACDD-1.3",
    "title": "Fractional cover - MODIS, CSIRO Land and Water algorithm",
    "summary": "Vegetation fractional cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. The MODIS Fractional Cover product is derived from the MODIS Nadir BRDF-Adjusted Reflectance (NBAR) product (MCD43A4, collection 5). A suite of derivative are also produced, namely total vegetation cover (PV+NPV), monthly fractional cover and total vegetation cover, monthly anomaly of total cover against the time series, and three-monthly total cover difference. MODIS fractional cover has been validated for Australia.  ",
    "license": "Creative Commons BY 4.0 - Rights: Copyright 2008-2016 CSIRO. Rights owned by the Commonwealth Scientific and Industrial Research Organisation (CSI

prl900

{"output":"metar_wind_spd",
"input":[{"name":"gfs_wind_dir","type":"cir"},
         {"name":"gfs_wind_spd","type":"lin"},
         {"name":"gfs_rh","type":"lin"}]}

{"output":"metar_wind_spd",
"input":[{"name":"gfs_uwind_spd","type":"lin"},
         {"name":"gfs_vwind_spd","type":"lin"},
         {"name":"gfs_rh","type":"lin"}]}

prl900

{"output":"metar_wind_spd",
"input":[{"name":"gfs_wind_dir","type":"cir"},
         {"name":"gfs_rh","type":"lin"}]}

{"output":"metar_wind_spd",
"input":[{"name":"gfs_u","type":"lin"},
         {"name":"gfs_v","type":"lin"},
         {"name":"gfs_rh","type":"lin"}]}

datasets/eddt_clean.csv

prl900

{"output":"metar_wind_spd",
"input":[{"name":"gfs_wind_dir","type":"cir"},
         {"name":"time","type":"cir"},
         {"name":"gfs_wind_spd","type":"lin"},
         {"name":"gfs_rh","type":"lin"}]}

{"output":"metar_wind_spd",
"input":[{"name":"u_time","type":"cir"},
         {"name":"v_time","type":"cir"},
         {"name":"gfs_uwind_spd","type":"lin"},

prl900

import argparse
import datetime
import functools
import glob
import json

import netCDF4
import numpy as np
import os.path

prl900

Experiment 1

• Output: metar_wind_spd

• Input: [gfs_wind_spd, gfs_wind_dir, gfs_temp, time]

Airport	Method	1000	500	250	100	50
EDDT	Linear	1.3006	1.2544	1.2246	1.2096	1.2230
	Lund	1.2870	1.2310	1.2002	1.2172	1.2616
	Circular	1.2870	1.2357	1.2155	1.2094	1.2205

prl900

Experiment 1

• Output: metar_wind_spd

• Input: [gfs_wind_spd, gfs_wind_dir, gfs_temp, time]

Airport	Method	1000	500	250	100	50
EDDT	Linear	0.6954	0.7079	0.7149	0.7184	0.7165
	Lund	0.7000	0.7137	0.7208	0.7169	0.7065
	Circular	0.7000	0.7129	0.7175	0.7193	0.7164

prl900

package main

import (
	"fmt"
	"github.com/golang/geo/s2"
)

func main() {
	
	// Definition of LatLng slice

prl900

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