{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate the skill of a MJO Index as a function of lead time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### In this example, we demonstrate: \n", "1. How to remotely access data from the Subseasonal Experiment (SubX) hindcast database and set it up to be used in `climpred`. \n", "2. How to calculate the Anomaly Correlation Coefficient (ACC) using daily data with `climpred`\n", "3. How to calculate and plot historical forecast skill of the real-time multivariate MJO (RMM) indices as function of lead time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Subseasonal Experiment (SubX)\n", "\n", "Further information on SubX is available from [Pegion et al. 2019](https://journals.ametsoc.org/doi/full/10.1175/BAMS-D-18-0270.1) and the [SubX project website](http://cola.gmu.edu/subx/)\n", "\n", "The SubX public database is hosted on the International Research Institute for Climate and Society (IRI) data server http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/\n", "\n", "Since the SubX data server is accessed via this notebook, the time for the notebook to run may is several minutes and will vary depending on the speed that data can be downloaded. This is a large dataset, so please be patient. If you prefer to download SubX data locally, scripts are available from https://github.com/kpegion/SubX." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Definitions\n", "\n", "RMM\n", ": Two indices (RMM1 and RMM2) are used to represent the MJO. Together they define the MJO based on 8 phases and can represent both the phase and amplitude of the MJO (Wheeler and Hendon 2004). This example uses the observed RMM1 provided by Matthew Wheeler at the Center for Australian Weather and Climate Research. It is the version of the indices in which interannual variability has not been removed.\n", "\n", "Skill of RMM\n", ": Traditionally, the skill of the RMM is calculated as a bivariate correlation encompassing the skill of the two indices together (Rashid et al. 2010; Gottschalck et al. 2010). Currently, `climpred` does not have the functionality to calculate the bivariate correlation, thus the anomaly correlation coefficient for RMM1 index is calculated here as a demonstration. The bivariate correlation metric will be added in a future version of `climpred`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "plt.style.use('seaborn-talk')\n", "\n", "import xarray as xr\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from climpred import HindcastEnsemble\n", "import climpred" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function to set 360 calendar to 360_day calendar and decond cf times" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def decode_cf(ds, time_var):\n", " if ds[time_var].attrs['calendar'] == '360':\n", " ds[time_var].attrs['calendar'] = '360_day'\n", " ds = xr.decode_cf(ds, decode_times=True)\n", " return ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the observed RMM Indices" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "obsds = climpred.tutorial.load_dataset('RMM-INTERANN-OBS')['rmm1'].to_dataset()\n", "obsds = obsds.dropna('time') # Get rid of missing times." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PzTAnwpszwR+/H0sB9Nvuq8CEDc2lXSZyrh5i+bLQoBsOIDvjFxPHxepunHyXZ0TUjFgjbe5ojkrTkNBSbwjx3BPRN6IarOEvGfHmzL4fdUuPmsguBcpQ9nae9L7C79fE7Ipp6hQ9gdTBbHXWUxxC+M80bVKP/GLlCvDJp+aWH//vf8AP3hvivjsShPL9nWjjJzHj1wzk8Oz8H5aOgTOIqFZRnXQS+OXnakQ2HpDICVL8wnjRRSLuvjHiooged5usQxVCQCwypKgWRZLi5+pXukw9c1X2EiPuvQWY+Xx++f3zgtq/iScgSNZXF8/7bdQq+sJTRpMztpPdl47433+Bmc9BPH4/xLw27XSIckGKX5gYGwZx5w3RYaPq/otPQ8x8zrxsOoyfYD0LMXUK+BG/AH9kuvW87BEaffzLNDEfA/kTqmyMNZdi58pAs2Be0RKgftlftmzCM8ZRSTtSzoYpfVOx8V78s/ZhzwapNGY9aQZcGdHKic8Bahg+6aQcBcmL6IbeswQlrpqcpzDqJHWcXmhU8HeCrowYWqc11MvOLzsbgqt28j3JOVIWRcrhomIUkIlBrFyJ6lnHgYd3g/rKLWiDlrfRou38HHxHQMZ6a/mZYt6JWNIBfv0lEG+8HLqRLV2iOEjxC+PKwGaKRnueTCR1QAmbO1wtQz29D+LJhyCeecy4ONZxZvxwQJCkjwAGiPtuB15/CWL6bUEFz3/6i9+mOJhY4Be/9WpUj9mv12m4cWy0rzRlw9U2nURBp+/wqy6AuP8u8DOPDd2R3dVrXCQiI6T4NShi1Urwm68APnyvaFHKQdJA4MoXa3hzh+D6A1hH/Gx1KaAZvyBhm9SlS6LDDRgYjDbnQ/Bbr07cTCbuuQmY1wZ+3cUmJM0dfuNl9RcLeofVm6+EeOcNzdjmbPzEsk7wx+4P1pO4NvXqi7HpEOWEFL8GRdx9E8S/o3wPElI42afpOO21qCA1a8fv4nOHZYrzt+a3V117XfA/Hwpxz00QM+70hY/JY/nSzGIWQe9uaEdm+fhph+tFNFjt+EVnQFxxLsStV/ddO+kAiMiJgpjVD+l24GB7aXJI8QvjXxZZ1lmcHBn7KPGu7ldlYyFW+dyZJK70lsGPXwgpvS+uIpXg+VzFhaJLPLmDIXgEW8ImjpXmXfKIjsWYf85J4M9n39WqbYvakBiseFGnUVWr4BedEZFtzAkdLn4AEVKQ4hdmYd+OPfHkg8XJkRk3vm6LhP/ndvCD9wZ/+pH0wEmdmET/ZszVQgi263f8mYQylVjqjdX7DMibe8fvykDjihw+QjZ+oZsxfyvSXdf4PTfVZoxWr44M1nX1ZCybcTe6zj9ZP69u+Nkn6EWMesyyKyoW3LnUsXhhctw6P6Lq+dnqKwl5SPELE+hAy1M8orMj2BHb0PtK1mDF1ClAtQpxyZkakeWfld9+HfjhP4OY/a56Pmn09x+uE3FyR5EUunTW5DZ+iTu8WaTXFjG/Hfy2a9TTDtwSELdeDfHMoxAP3B0d5p030/MIkPAuwztJm5k8ql1as/Iv92vII+a3gx+zH7puuFQ9MmGM8mg2eeFf6nXEJiQNMb8d/JB9wU86oO9iiZTW/FDoqRQGd3HXDUDHIvDLztaQKS3xpHsZbPxM1O28FSAXFK6c4ddeKBewzuF4vebHJ50EPPt43XUl/PnEOW2PWxosGoP9ueAcwtTpSvK55pxfhAQ3TvH9EOD//Q/ES89Ix+c3XArMbweffpsF6QhZSDtIoiyKX8+X99w5fTYxJZHdGUwoFbbPRa7b1SsklnqpHhgn6gxWS4gHp0kGTKi/PU562z6UzTXmegPVpYztXUw5B/zIX8iZkZjClEKt7Kg5GvHGyxD/vAD8/JMh/Ec5JrFcMhxhFVL8wogCZ/x0OyO/b66uLjOyED6K/9IGUKsfgToigKVpG5As1uG824eOfZEFhBP1IWFzR/j0QVO7+zsXA/N8vvziisGUKVpXtA2hLYSCQi+eerj275Rzav8u7YSY325Frr5MDaWTdLymyqKIfwdwknuoJpypdx1S/MI4eVSXAivM79JrGBI7ILlTMUTbR+DX/APiw/frb+bsiFb853bgvVnJ8WNEEv+5XWmgc4KwHVtRuDiO8XDZ9P0W//4XxKw4Z80RxD3fnA/Bj/utRHyp3VCJt/njD4Af9KP0dAzCzz5eIxaD4FXwP+4DfvSvIdo/Mi5XL3koUCoueyq+CYdqwoQDKX7OUULNxjJV/4xf+YqHHzaxfAN6DohFC1RjRF7lZxwF8dC94CcfXH/T+gxYcGk34HsNirvl5rfXDmavViEWzDUlYH4UuurowEBWJ0KKInXG0dZEqWOxYluLOl3m8kkZVy+iyiOl0rzxMoROnsv7PrbFkw+rx5fGgXrnR0ccUgKdoF96kCZD+Owf/F80eZOgRAjOIabdDLbmOmBbf7E+gN93XQKiq6u2a26DTcAGDdaVNJhm93Ikc2y2lB/x8+QAspsoes5nLsqAPdHtTITNX5IyOutV8KceBmY+D+/A48G23NaMjI1OQUdnBdA6tzmchvINOzhQnH0kCyOEADpDp6Lk9RHiVDlBvg8kO2PncGt0dgH/bJnyLFE+iKcfgbjtGvCLTo93cCrR2MRNl4NPOhH8glOMycb/ekTNA3yMjy9nqRtIdRKx3MGlyqQ4yyE4MPN5ACjJcVyujHyuyOGjblevBJmra5ZySPI7mIJNRSJFgRbXXwx+6L7a8VOzn/MhxJI4ezkH610vsu/E7DOIZx4Dv/4SiJVykx1EDZrxC+Ny2+rhg3f6/uYCiYbeCYj776r98cYrRsQCAHSfQymeexxs2y+bSzdvdDpwKwOSgvNd1dWtKpkEaGFwuUqsWglx77/APrkZ2Kc+oxIz4WfWY3/y7gSVNT8DecY8Y4qZjHjgnoirZtq9+PB98D8dmBDAscHJAZMiftHpAADx/JNgm20Ftvd+YKHzqIl6aMavDscaFwDxynMQfmUvbXejC4/gQKegRvLGDyn7uTxWNBKXehXLPMkgWyd/6+S3ZCQWzgd/8B4Iy8c2imk3Q9x5vebGAn9Cfttk5NIHiPvuAJ92s/2MwhipBnG7nmI2dSUdHWdot3nPTuH4AC507Bq0fyTv7kWXBXMhHpkOce+/7ObTIJDiF8Zg4+K3/DNzGmLWq+DnngR+yh963RuI99/2BYiKJOHfjQiSYCwv3n4DH+3zNVTvnpqSiO2lXo0Zv8Sl3rINJPnJy087HOLai8CnTIq4ae6jRuieTKG4uUMtbQXn5f+6CmLpkvSAdRHVo/RitW+rF4xfdAb44T+P3yBmysuQl/JchbVXNSW5jq4u8KN+pRZHlzmz7abfIBhd6r3mmmvw7LPPYv78+Rg0aBC22mor7Lvvvhg2bJjJbOyiXDET3G3cewvwvZ9lSkt022ABAFatAvr1B2Y+l5K/AwN62RVPXxGKd96o/TSgyGeSQ8vGz3BdcOa9Wpajx9fZi0/bzcfUcwS8ucilKa6/xEzeOva8OjaJfRHU88uAeObR2r933hATwFROac/lQL8eQMFOc5mCm5gsWFIs+YP3ACtXwvvGd62knzdGZ/w8z8NBBx2EKVOm4Mwzz8SCBQvwj3/8w2QW9snxq4rfdwf4lefpuRDoQYgIE7+Y5YrSzfDkiYGysW3jN68NSHJqq/p+w86gCTlMtiNTdSawzC+51hs3c1ntgpj5PITVk2iybO4wnL/EZQDRba9OFr26IXi1dvRjYiCtpA0QV1Ymd5IbwsIYJxbMg7j2Ioibr4B4U8EfpsMYnfHbZ599ev8eMWIEvvnNb+K8885TSmPJkiVYsqS2dDB+/HhULLtU6Um/51/Vw2w8z0uMEye/EALVGy+rpbHuRqjsuifA+tKqeB5YpQL40q9UKmCVSiC/iuehyligPVUYgwhd603T52YlkE63nF03XIYqBMTBx9XJHhU+fL0Hz/Osv7s0VN6l53nwfPIKjyFtQS9cZxhj0s8sqlXA88C6B/4oWSuVCgT63qP4V/KMY2+d8efjebHPwQJ1RE52zvrKxatUcnV5xH3P4lWC78s0cXUdADjixy/VOs9970ClD2IsmFe4bKLav7RMU6cAH88G+/SW6HfEaanyVCqV2DpWqVRQvfcW8DdeQb/9j+h1G8WZT16vUvcuE/Nk6X1LOH6lUgmVUS3PunARbcjfvqNW2Cu+dJmvHamMC1WJo9+iZFNBtj+s7/ejNW3/50XFq8SWmz/dcNvJOkaE81Dpg2URXav73u+c2ah8aguj6QP1eohtrO7qfemll7DuuusqxZk2bRpuvrlmMDx16lS0trbaEK2OlpYWAMAHivGGDx+BRQn3x40b1zu4+xGco8caYfDSDoxubUXniBFY2H1tjZax6N/aisVDh6DDl5Y3dFhAxnHjxqFj6FD4rWzGjh2LBQMHIvy93jpuXKBx+tNpbW3FqjdfRdv02vFOq77xHbSEKng4fNT1HkaOHImhFt7d4msvxooXnsbYE86BN3xEYliVdzl61GgM8sm7euVSzEkID/SVQU8+lf79pOprddECzDlgIgZsshnGnnhOrKytra1YOGQwZLcXjBs7Ft7gIYFrq1ctj32OgYMGocf1bKVSkZJ9+ajRmNf998gRIwJlZpvlI0f15j1m9BgMtJh3XF0HgLkD+te1rbiwabQPHIgeRxTh+pTEoEGDsIYvr1Ud89HW/feIESPQ1dkBDcu7Gh/Xeibx6gtobW1NlWfs2LGIO6+itbUVH0y9vCbzg3dj1M8OAAAsH933LkePGVNXj5LyZB5LLefZISWttbUVy0aNRM9hZWNGj8bAiGcbN3YsvGHDI2UZNGgQ6rYoMIZxY8ei5xTkYcOGYmRrK4QQSLI2C8u/BDxxHAGAsS0tqKwxNiVUPLL9YVi22Sz6Q2dA//69dXeNljXQP+Udtra2oq1/P6zyXesZd3UJ5zFw4EC0GO4XuvhqfNz994hhwzDMYr+TtTxksab4PfHEE5gxYwb+9Kc/KcXbbbfdsMMOO/T+bmtrSwidnUqlgpaWFsybNw/VahXeLt8GD52IkMSSjo7E+21zPgaWLQMLdSb+0zWWdXZiVVsbqr605s+fBzZgMLo6+2wj2tvbwYYEbSXa29pQXRq8NrdtDroi/Bq1tbXFfjG2tbWBv/9u72/e2dFbJnHhk1i8eDE6Lby7VdddCgD4+NJJ6PcTieOjJFm4cAE8n7x83ryE0DXCZVDtqkrV164bLgNfNB8rnnw4MXxbWxu6lsrbxrS3tYGFFD8xL/5UjpUr+tSXalVOdr64b3havHgxFltun3F5Lwi9L5uEy2V1gs8w1f5q9aq+YVAl7orlywPh+fy+81c7OpZALDezi1JGprkJdcwff+ns97Gy+zdfuLD3+sKFC5XepRDpcoXNWtra2sAX9fnHWzA/uv60z20HWxpddiuWRx2HKdA+t++M3s7OpVjR1pZ6elJd37EkXU2fO7cdrMu+t4SwbHGrp6t87WD+/AVg/ZMPAWhra8PqkD1o0hijw4qVK4zrDMI3FnQsXoylFvqdsB4ShclJMCuK3+OPP45LLrkERx55JDbYYAOluMOHD8fw4X1KkslKEQX/4B0snnYzurb5Mrpuuya4mSINxsBTXGh0XTYJ4okH4B1yCtimn+297ncPIKpdqFargbSqVQ5WrUIErnWBhcqjWq3WdTLVKo+056t2dcWax1Sr1dryY++zeahWq7Hln/ZeOOeAxXcnOhYlyqDqQJqHnl9I+LgL5y8irkXK5nOj0tXVFTkj3JO+yvF71a76+iGSyihk4yclu08eznli+qbxvxNezS9vf7mIV1+AeO1FqbAy+N+ASlzBg+/LXxbVe24C1lpHSY44ZGSqJrSVgIyC9/7OVI8YS5cr1P+F21K4vffQ9ch0eLt+JzrJyLbIUO2qBsLU8kqWLyw/l2jnUe3bBrL10F8e1Wo1VbZqtVo3LiWNMTqE24WZNP11tWp1XDNdHnEYV/weeOABXHXVVTjqqKPwqU99ynTyxuk66cDaUuoNl2mmkGxpLJ54AADAL5+Eylk+Gy1//Zc9AioyWJQfP113Lj6LL2d2b2qyMH3GLg5+69UQr7+kHlGnzNLelZKxch7O2xxI8TAAACAASURBVLLsxjRIjnmLV18A+/SWEHPngJ9zQm75KuFXHOa31/5zDt87y2KEr/vuA9FiNsDdOAWIUfzi8W+950nJxyPzTK7tv0p4h8VtJLSQr//40QbZIGl0V+8999yDq6++Gscdd1wplD4zaFYEEdFZyOalfaZqmqx9nY+LO4BFm8+SyCef4FWI8ECnOjh0pyfmz4W45ybgrdd0xUyHKXQkKu8hizcXndftYB2xQa+y9+F7qWFFtQrx8Wz59qOpxIiuVemBXKYIxS+Qf/YkANRkifoYstI2XGtvduURbR9BdCabUhF6GJ3xu/LKK1GpVHDyyScHrl999dUmszFGJjcqunk++zjEUw+D/eCXETdTInfPDLJhw/t2ev77VqAz7mxHxfQVO1Tx/tvgV//dSFpp8AfuhvCfJ+vrWPmFZwDPPwG23+Hweo6J081/VZYzH9XPSBVPPwy23c4JgVUUPzX7HxE1U5FG2WeCM5H+LvhFpwPPPwn24/3BvrqHRJqa5RleDlI9taXU6NZBU56WZfKwkL5rH1qJpwhlcwEjZr8LfvLBAADvktvrVqBE12rwy87WTl+JQJqN0f8ZVfymTk072cAxbB8jEwG/8K8AALHcZ7Sf2IAifvjH67tujI4U+aWk0Ci6Vtf8ePXrHxuEX3EeMPud6JuGG2BA6Qvz/BO1MP+8ANBW/IRmPB+yUX15iCmTIJLONM680itrRqDxvkqiBIpZrwKDh4KtPcF+Zs8/Wcvz+ksAKcVPk7BNWJE6gUa9zzbjpx+1F53TV+LyzcFvei1N1xQ/7ZvpST9+v+9HvTmMeOQ/wDOPmc42RpgMPicdpamPbGPDRoBtWNCStP/sXdkGLRmOX38JMOtV9fi+Sj3vlEOx+tCJEEke1+OUvqLI8mXWEzXt2KQkZBWhcLhEG0/5noz/4zTwR6ZLhw9GlhwIXRt8UhAfvgd+xlHgfzoQImE3bmG8N6v3TyXzinDYkr2XAMqyZx99+TkngF97YeZ0aqKY0PxKqFEEZpnD9TEujsZYFxWnY2H9NaCUxVgETa34AcjogJbpx087NSGqAss2mmcfj8lTLnovy5f2HlfkGiLqYfzKSyEdgKzip9DsZDf+AMCsmRBXTQ5es6kQyC7brFoJ/sh0iPY4b29m8wtEefWFvh+yJhGxiWWLHonOObdA/ceDpfds1BxGYnOFFMs6IebHu5CRRTw4Lfq6335YtVx7V3qbYKlXEbUPG//f0bupIzG4CiGEcNLW3QSk+GXVEKSN52WXczXTsIWry3mpy5p6mztyQemYJ5uKm0Ycjfogbr8O4qrJ4MeZ87tYiGJvu46opF/3HizJ9vIzBhPzL/X6r6vLzv96eGZpYvErGjLvJLe+wzElJDArl3BPL/HktCy3f9G1Gvzkg8H/dkxD2s+S4ucC0o2kx8ZPt1HJ7+rtu6TrOqFghTGH/Pm1Fwa/CE3maWx3oOHBQuN5xWMz6q8t6QC/7RoIXXMBx8ZAM2RQ/GwpHyn+6GrE14PYGZOs8i6OWerryyD4q2MheEQ9jCR2lj3qOaPPRRYvPC2Xlwoqs/8ZEO+9FbwQ184T32HEvWVLgXfeCFziLzwF/tTDyWlHPndcnTPUB7/wVG0X/6yZQbOsosc1Q5Dil/VFarttSFnqjSJrw0+18Yvr2IqFPxrVYcvbK8qhsYz44LRaB5EVm/25CwpSRL3iV5wLcfdU8JP/YDlzFwogga226/tbRe8b/4ngBUuKH0/aVCVDrFz5vhd++lEKbVWxbw4sNNTqurjkb7KiKZBPmfE/HxLKVkZ5T7fx43/cJ/C7umgBus47GeLSsyDef6s+QlJicRgarvwmDsI/7jbI0i8pflkqSqxhbwThChOoTFHho26KiGsqaMQrWO8TixZAXHmeWpx5bYCqbZJmkQq/kbH2R0TCF61mRyNWLIOYm3basAZSM0BhYSKe4aX/ZZOjp3iWdiqc+pC1Mpvv9JnnsxFWeNdi2i0Qc3ynwdoakFJn1gBpU4UepahahXjz1cggxginqdIWkpYww4R3m370vnw+qrimc6jO+IWoLuhzsi9mh3xk+u21P/5AfhbSCpZWdQqEFL+sGGmMkku4jq36JedlKLM4g/yY9MULT4Mfsx/4WcdpZqi5GxiQ7xTCoif5DtQsR37CAeDH7g/h2zWaLohEug9EG8QXgZgzG/yP+4CfcZRsjIwZZotuGv6Xw/p+FDoTkXTyTMSlW6+CuO92e+JkRXVlxV/2zz4OobthJz0jS+lqkrbz1lDa/C+Hgf/5EIj2j1OjMc2POzHnQ/D/3A7R4+It9nlI8WsQcnqRysdyRfWYGY1MtRpn0VN+CteFAJ86pfa38pFVmrOpOv7Awjn/9776i1l3By6aX4t+3x168eOY+Zx6HGNfySG7rTu7fViG7IZiY0+7GdU/HwrRXTbuofiuVyz3RS1SKUjIO0Iu8e9bU8NI5Trz+eCS3IK54LdfV5vxz4Lq5o7Q8/M//iRb/rHZ1PIRixagOvnP0bZxprPkPOHDVO29K2Zcf+nNV7KlmQA/8fcQU6dAXBth2kB+/BqQLIMS58DK5enhgIiGEFxOEM88CrwV4XsvMY0cyOLXzgR5PbNuPv4OStqPX+j36oTjtzJ3oI7NEphC47HEQ/cC782C0D6X20JZBpwaq0evnngAxAtP6UU2RWLW9uTik06EuOO6vt9nnwBx1w0KM8AxxNmuGbSB9p8FLt5/G+KGSyQi1f7hV00GXngK4tKztPJWQTxwT/zNJHMl5dcuEUHGXZfueN6jVD/xQL08DdiHkuKXEXHLPzUj+v5842Xwi86AeOJB38WE5V/teljOzR3SGJlZysENTNgO+n//jRBD/+inOnuYRiRDZyzinL9azFMyA/UoH38APvnPxQ5Osq6qLNhHiWk39/3o8RO5aEG28nj3TfBb/gn+8L8lhVDPgp91XK8NLj/1j5KxujOyaUcYzvHuqJOheu9q3usmqT5E3ZNZ8bJRx6KOhis5Ro9sIzSJWnqKXMrMutSbcGtpJ8SHER1K4casCmu9QiSEl80mw1Kvbll9+F79tV7FXz058cDdcjI14JesFC49tqn25epSr1Rhu/RCAPHKs73L0WzrLyYHZtAv+w/eAcauqSBYd1/j5Thfk5TX7Hd9P8Jfs6p2khJhAraXth352U2+aEjxK4wsM0W6tTI+Hj/i59FLjitXaOZVNjTtjKb77JV0l3oTxdGRq8F7LQCAyND3a77rtGjDRsSckW0qA0txs5Jk51qYXBnyXbig7++AclMwhRSlRQXL5KkwuVH0RIgZaKm3qBmt5ctSAsTNaGUgKX6cnZm1HWqSxIm8cD7E269HhM/aOyrWh0UL0sOEkZoE6R5MDSwl28KKuxjpzFHAQGg5wyzJF6j48aN+FX/Tb9ISd2yfa98pPrvmgHeA2K4hZztk35GP8q6MFLP6+IOevyQjqBv5dfo3+dS5iYo8s1ROFhPEtafCV8DMQIpfiV6keG8Wqn89AuK5JzUTUI/C1lxHLy9jxAj93izwvx4RMJIufuC0UJe0FL98jhiqXnl+Lvn0Ymry09kl7kwV2JgUZgnteL00J3upLMWhMiYwlmPRd2fkU0zFv/9lJSd+4gHZPuwkyqQ617f72lSb1DlS8v23o65ml8VhSPFzlSg3CJedDbz9uvxO4gT4jVPkAjquGAc2xBQtqmz+KnLqdIgm7A4dQlSrEO+8CRGYFciy1KsrSMq76FptN/0kcjrOS5nwR0gZNh6xmGExroiXLLImSmT+/hm/+++ylh2fOkXSgTeQ2cZPNYvYfk29UwhvruFPPQxx1WRTyTsJ2fi5/iaNtp++xJx2oOon1am1qdktAwWt68DZNIEyc7x+SyCuuxji4XuBIcN8FwtQdNLyXKHxQWbJx6Ez1IkVOQWbR8bZiXpVy5cFHWkroSpjzu/4ec2VJQDKu3otLVnrkIebnKKhGT9nx0UbX0yODg5Z8M90MKb/jHkWTZLfvjBaS73+MlGPngTb4WtmE5RAPHxv7Y9lnSFhzD2cWL4MImHGjj8d4XLHOFk2d5iTwijh+luGPsjVUxsi5XJkAMtqfn73VAgh+mwWdR/LkeJwHZrxcx6THWUJOt0wqSL7rceLfj65XkfMuFM+ycyKn9pxWqmsva56nCgZsijpQC2uofctOhaBH/NrYMw4eAedAPHUQ/VhLvkbMHCQkfziBckSNx+7TnUkHsrKsqB5e0nx7psZ0jRA4f2bAqqiLl5Qc7y9YB68UybDXQ3OVbnUIMXP1RdZktUP+6Qt9YZ/68749cTLUEg2nIfqOHBWPnJKKXXD6WUgo5d+fuNlEC8/A7bpVsCqVcCc2eDH/SY+nm3XRlmcUjv0WgLI6H1PPgR+5w3wfv4HsLUn2JcpjTg3I1mPgstK1EvWdUZuGo1dvXW89RoAgB+0N9jO36q/P09is0kD2DTnAS31Oox45w2Ie24ymKCro0MCKTIL/zF3mRq9CPxjE7bT7vKBs874NbDel/WjTdx3BzDnQ6sG8umYcuBc3hk/8dTDtdMyLjglB3nSEU8/EnPDcOVXNvGz4OLLGGaX9MXcj+uvTb8NYtnSTOlmpkEUS1L8XH2RQoCfcXTRUsCxkb6eIn3J6SLpeZ9fcR7w0QfpAesiWnxnrgw0WeRwtc1nsvFz5L2EUZFrfrvBfM0lZTnR5iWpHcbNrn+c1h+62rbdghQ/l+tJ1bRn8xJ2XHkNaL0rve6UkXhsBjBntkZE/+xPwvOkdqKRiWvEMY94/SXfgeqAUHlvDr3jAI6KlYlGeqbNty42/0ymAJZfhM5Kb5JMaTvkUz7eRGdH4matZocUP2ex7AcpI/zeW8wl5hR5rPU68rXR9mHgp1g0H2LWzORBIvMSjpkZ2joTiDi5XFXyeghUhSwzfuVd6i1Lvqyl1XiaajhelwPIGHfaqbOi/WPwQ/YFP+WPdff4I9PBH5xmJd8yQZs78pryUx2AdI4CMy0DgLjyEbf8MznaovkaeUVlpCKzARu/RoDrbe7gR/wCAOAddALwmW2iA+nUIZ+iy4/dH95vj1JPIxU1uWwddVUYrlZf1xXvQlEsG4GaE/Osdr9W0LDxSwqSFj3u45mh7/z0jz+AEAKsO6xo/6jXMbPY6FNg66yfLmODQjN+jsIvONVCqvl1wuLmK1E96cDc8uvLOOOu3kxeIBwZ5DLKwR+9LyHtTEnX0r/ojOyJhFGVq2gj8R78A1gm9zbNO+MnFkZ8ZFppi8XO1ItF82sfZzrmHznAZ9wJPmVS9xJrgX1hv/59f/s/ghf2TaaIGXdBfPS+etrd7ZVfeR6q551c2g9IUvzyWnZzYXkv77b40fsQq1ZmTCRvoe3mZ93WBgh2dlok1VVzs8Zx8NuuAb/tGrUsVG38HGiOdZh3PVc8eVT3I38B8cxj9jMq2n/zHdcBHXLHw4muLoiAnZxtGz8BccOlEE88ADH9Nqko3vARWvkkw4BKpe9njJ2f+O9/wDUnJsTcORCPzgBefgbi2ce10igaUvzyasxOzAbpyCDpDDgu9n//A7GkQyNfDRig28Hloo/dPRX8sIkQts8rtTn7o7XUq5D8B+/UvPjfPVXRYa6iXHHnsRZKA2p+OfV7/IrzcsmnUFbLbVYQXavB/3Y0+BE/hzBlcqNC+8dS1XHw9jsl3I1LICVhxhDocGQ3uqngVyZXLDOTZs642PsRDiI0T0oQ118Cft6fMmSsGFbblYnQfkbpHG67BliyGJg101oetYwyPkPihJ9G2ir2qp2+jwTJ2Q0A6n26CzPwAIKDVIb39qblOqVNXgppHvm4UmfiEbPfAT9ob+CdN4AVy/tm3/I08eNcqi5XkzZ6zX43JXaM7fljM2Jn+YxQp1g6+sGVAil+eTVmFwYazUoqVq4AP+H3+vm+Nwti5vMQrzynkbmyl1P1PADguSfA//iTgIuQ0hJw4KxeHgwMYvFCVPfbE/ymKwwKJoHnW6ZRWrLOqZ44ikiyyyySxipm5+FXnB9UfAoof/H4/VIZL7rsXI3EJYL4HbKnhBfKZjGsDPp/KqT45aWQufBloLUbDBCPTK9z/aEKn3Qi+LknGXPnEQmDvnL79CPAsk6Iaebd1AjO8zUCzqj4idWrwA//We3vnh1yGdJTwu/cWqVTVpXLgeYIwJw7F1dxdtOJBkV/vMfU8epxv4XoObqNh/uZnk1rOe/q7bRs3iP7KtKeO3O/XM42S4ofkYwQZqfOlU+iUB3Q3WqI4tnHwP+4T74zMjqbO/ye8pcsTgiYn+InXn5GPp6yWG7VEwBOipSZ3FZ6HSm8wUPyz7P9I4jbru3+Ua8RCc4hnrW8+aWuz3FlWsxsvRBXnhdM0pFqpwopfs5U0BzQ6hyF4cptf3eZS/ALTweWL4O4+u/5ZeqfZVmZ4gG/B5/hOFtvI2Dsmr2/q5NOBO9ZAs+xeMWTD6mEVrvuWD0B4KZMmWmgZ5IZKqyeIJMQvmcHb1hGISAevQ/i0rMU88qKpfdufGZfY0XkuXLu5PVDil+O5OLKI1kCvShLDU7bl3VJTgoHhV0wTy6cX1kcMDB4b+bzEFMmdYfLU/OzOIgW3hZ7aPAPz9zK2ZH3qfC8ykWT1JYTEhNPP6KYkQbOvudksxfxkMYpHss6E9MsA6T45dnvlrKSCIh/35oeTCE9a+G7uoDljjjmLZKsfvw6O4BYW0xHFb8eGycbaeeGizJlRLGcxQtPWxLEBBKDhd9kIo13Xjc/GRBObtVKA3490+FnhE7jsV2VZe0tU8pX3DhFPW9/knNmQ3w824FJHTVI8cvVYLfgyqG10mu5YzKdf1eXYgZEGPHojOjrK1eaqcLDhidkHvxCF8vl/GTx436rKISDHbWDIuUNn6x5YlEJy07853aIx+43k9Yzj4JfdjawdEnwRt1mj5ywpQg58Z77hBAz7gQ/MYPHi4IgxS9Piq60Ojs8Tctc2iW5EmGpzPgffwzxxstW0gYAfuNlwZkDxuzZRrpYrTK8N7b1lwwKYpBGar8WJgnEvTcbSkjUbGIXhpaDR49trHfQi+yMn4WsI3zFsqJ3fCtCil+ea72FN0CN/I17fm9sxY8/dj+45JFF1rBVZl1dgEXFT9x3R/CCx+zYJ1l21K1NFplGt5iTwyg5lfPqVfnkYxrrxSPyyCQmXwtUV0PwqkL6NuQIpVkypQ8gxS9nyrfUK6Zq2ECYlMHFATqOjsUQV5wLcdPlEK+/VJwcjeI7zeqxao7UK9+gwS+flCEhR54njKNi6WFhgLfdvwmR4TSjLPnaSZZPOgn81EPln8lC+Yq2j4IXSPErIU21uaPo/IGGnvHz2dfUdQ550pHkhy8/+L23oJrhuD621faZ8hdCRG9SYcyNphAmy2yqq+3EVbl0sDJW2Fb8csgjCpsbSma/A8yZLRk4eVevFi/9L/jbyXO/kymfxIZhOWp+uWyrT4DfcBnEay8WKoNQ/fos08AR+PIrUO4iDmaPQNzyT+DlZ2PuSrS7NdfOlv/0WyEejHDX4OpSbxZy2LmpRwOVs42ZHeszftVi6rorqw55PDrN+BFJiCvOK1aAmc+Bn318sTI0+ZmqRA/236u4+cpC888XR59HQyzu6rnDNrCslIkH77WafnzGluujqSPbTECKXwkp4UsrNaoNsQj7FF0abRapZAiVowVdeVWmuh9n656GJ4Erz7cgh6MsWmA3/eVLi5kNdmXGLw9KqEOQ4lfCl1ZqTB5T5Bp+n3MlErsYLLjGuOFSuYDvvlnzedZIuKr4uSqXFhbGCpPnoLuEMx/secz4lU+NKp/ERHNRpoGjUTtxK/icoL71WrTne8WPMvHQvRCyDrxnzVRK2x6GlAlX24mjYjUVBdQN8cj03POMJI9H98o3eUSKH5EvzeLAuXx9QWYEr0LMfhdCcWmJn34kxP13198YNFhZBn7mMcpxisVQ/V7lqB+7lcuLlsAcNtr08JEWEvWxxeeLUfyeeMByDmaObDNCCVcN+xUtANFsNIni12QIzsF/810AANv2y2D7HZ4SgyEw63fHtfVB+ml0T2+/rh7HIXTP/LQ/0OrB/3pE0SI0NWx0C8RixXOsG4pctvXmkIdZSPErobZeahrZgbOfkoqtjc9IXTz1MKqpg41EAWUoQ9GxSD9yroT6n2Yyii8dZRwrijq5wzKy4zYt9UZCil8TKn7V/fYsLnNFRU4Udcg4IQ1/cBrE46HD5pNOLuE8oh6Ya4dixXLwwyYaSy9XCvazSeTMEsvO1hvRZ6UStLkjClL8CGfhl59br1AQTiHeeAXi2gvVIi1bKpm4Zqc9+129eEUQ+vDkk04qSBAilbJOEjSz4kc2fpGUT1UlSo58QySlz33sbqbQ7LRL2BFn5jPbFC0B4SJNP+OXAyWc8SufxKZpxkGCsA91tmqYbIfUpgkbULVyB2kbvzxm/OxnYRpS/Ih8IX2IkEW3rjii+Onu0NXMLL+8iPLQoDN+zvgJBGjGr5y4MUg0D43XCREGMNoM3WjT/IhfgN97S3IgR5RUQoaSvqsGVPykyePZO8rnLocUPyJfmqYTapbntEnJy3DxAohb/plPXuQGxj6lVNJL3oYyk8PzV8vneYIUv1I25hLDqxBvzoRY7ehJAwRRRprmg4pQwpkzcwvC//jURnohdy556X10jisAQFx3CUS1C9h8a1T+EO+6Ilf7KKJ4OpfUX9P1cdaM33LUXAiinsA4Qo2kB5rxy4vly4qWwA2qXbV/X36mWDms04zah1nEnTfoRWzGWXxa6iUiIWWnFyqKXkjxowHaTUo/42dY/tEtZtMjCEKNsn5QlL4vzYLv2enjqJemV/zKc6Zns1Hyzsqw+GybHcwmSDQWTT24E7E0e7Wgld5ISPF76X9Fi0BEQY00SFlnG4qgGZWgZnzmvCllGZdRZpOQ5hdF0yt+hKOUspP1Y3rKj5oqkcDrLxUtAeEiQgAL5xctRXH4x5HSjynmoF29BGEJsazTXGI04yePyXK3Db1XwiLimcea26METfhFQoof4SjlbqXiuoshrrvYXIIezfjJwm+7tmgRCMINmlnpA5CL5rfF5+2kaxEaTQg3KbfeZx5a6pWn/eOiJSAaEVoqLDe2dvWW8KO8fBITTQJ1sgFoSVAecttAEAQQsvGzlEcJ+2ZS/Ag3oa/rIF75OpfCWLa0aAkUoPdaHqhPKh85bO4gxY8g5BGcZmakoaVegiAINUTsD3OUsG8un8RE49D2Yfw9+rgOUsKvSqJG4rnT9FrLA/VJ5SOXpV5L6Vqk6RU/VsIdOc0B9bIBSmhATHTz8rPx96ialwcyPykh9nf1MprxKx/eJzcrWoQmJuFTiTrZICXsXIgagnYZNwTijZeLFoFQJXBUL9n49UCjSQlfWsOQWPSk+AWgelpekl4dvdbyQKejlJAc/PiV8KO8fBKbpoQvrWH48L2iJSgPtKuXIAhCjRyObBPtH1lJ1yak9dB4Whj8ojPib9KEXxD6QJFn0OCiJQiSNFtLM7kEYY/AhJ+lQeXdN+2kaxEaTWhAdROy8QtCCoI07Cu7FS0CQRBOQONIFKT1xIynbOKB+cpBhKAGG4B29crT2VG0BCHIyK8UTNiwaAkI0+Sw1FtGaDSJmfFja62TsyBEAGqjQWhmWhrx6H1FixCEdLtyQLPqjQcpfpH0M50g5xzXXnstHnzwQaxevRqf+cxnsP/++2PEiBGmszJEXGNX6wTY1/eCmH5bdnGIGm+/VrQEbkGDUmNCr5Ug8oEUv16MTyPcdttt+N///ofTTjsNF110EQBg8uTJprMxR9xuScWBlu35EwPCEEDtpAN+/ilFi+EWpPiVGHp3pYDaWOPRreyJ996CuOLcgoVxB+Mzfvfddx++//3vo7W1FQCw77774uCDD0Z7ezvGjRuXGn/JkiVYsmQJAGD8+PGoVCqmRQwgKhVUI657lQpUTpLtN2QIurbbCfyJBw1J1hxEvd+uqxz+UCgIr9Ivsp4S7uN5Xmw/JphHVg2OwDx6F0mwjT4NMevVosVQwvM8eJUKVl1wqtV8suopPfFt6zs9GFX8li1bhnnz5mGDDTbovbbmmmti8ODBeP/996UUv2nTpuHmm28GAEydOrVXgbRF57DhWBhxfcyYNdCukE5rayuW77oH5pHip0TU+/3gwWkFSOI2I0eNwoKihSC0GDFyBIbF9GMLhwxGZ87yENH0HzAAq4oWwmH69+9fuvIZM3o0Bra24oPOxVbzMaWntLS0GEknDeOKHwAMGTIkcH3o0KG999LYbbfdsMMOO/T+bmtrMydgBGLZ0sjrCxaqDbNtbW3gi+1WrkbE9vttFBZ3LilaBEKTjo4lWBpTz7uWL89ZGiKO1atXFy2C06xe3VW0CMosWDAfXg5jTNZxrFKpoKWlBfPmzUO1Gr22Y3ISzKjiN3hwzXFqWMlbunRpnTIYx/DhwzF8+PDe33GFYIyY8/s4V1norckpFOMQObzfBoHbOmeSsA7nHIip59bODyWUEWTjl0gZayqvViGqVdi2szU1jlWr1VzGRKObO4YOHYqWlha8/fbbvdfa2tqwfPlyTJgwwWRW5oj1j0adQB6IhfOLFqEc0KBUXor6INxo02LyLS3UxhLJq3gmbJAeRpYebZVebQDju3p33XVX3H777Whvb8eyZctw7bXXYsstt5Sy7yOaD37kL1C94FTw+26H6KKlFqLxENddFH/TokLPdt7dWtoNCSkHyZTy47OM85T2Mb6rd6+99kJnZyeOOeaYXj9+Bx10kOlszBHnGFerjlMl0+LFpyFefBpYtQps9x8ULY2blLLTJTIzZiywYK5WVNqlSpilhH2QoCm/KIwrfp7nYeLEiZg4caLppO1A9cEZxCvPAqT4RcIGDKBBvBFJU+hJ4SdcoYx1kZw2R0LnQMXN+OmcjdpKx7wRlmAVeEeeXrQURFnp179oCQhCDhvKvMK5lgAAIABJREFUmk2ldeBge2lbghS/uAqhcTYqW2sdYKvtMgpEEBEwAMNcPfaQsIapAWu8o5vrCMImOcz4sS22tp6HaUjx8/WrbJ/f1P4YMQpYW6+jZFt/yYBQJUZnppRIhzF7ZgnrrG8pYSIrbIevacelVS7CKCVc6hXz2yEsN4QyunEzbuNXZljreLALbwE8D8zL5+iUhoN5gNJhd4QcTGsWWoqPP7CTLpFOwqDkHXg80Lo2xG3XZM+nhIM24Rh51SGDepq4ajLQsciuLX+1fI6taXrGD/PA+vUnpS8LJZrxYzt+HezbPy5aDDkY7HVeghR1J9lkC8Ajha2paF0bGDKsaCkaCiMfTkmU8KOqPKO0Ncr30pymRIqfN/FAsPU2KloMOZgHa3WV1gSLw2rZ03stHZ4Htu2ORUsRTQkVnD7sye59ZTdraduiPKN0HpRIaXGWspWhreVTw4h33rDX8ZLiVyAJZc9sTvMS7uLqO89LrpL1RyNHFy2BMuUY9XJDr2L3bgohyvdVWBZxOxaVr2yJjDD9dz5wkFlRmgEX2hdjcFbxcaB4nGSd9YqWQBlS/PxoNnxv528ZFqTElGQGrY8S9Wb++jl4SHFyEOZIGuMzVE3vtEv0IzcrNPPduLig1DtE2UZpu5ioHM1ewcr2/CU1nmdf/y7wyc2LFoNwFDZiFCkyZYQxd99bbrt6LTz/yhXm0+yGlW3MAyl+QUr4Ap2jdGVYNnm7GTQYlSNOK1oKIjMpNn6m2hOvmkmnkSld35U3VD6NAil+fqjhZ6fIzR39NNxSluWdl0VOwiAmFT9y2VMklZ8eULQI2aE+qGEgxc8PVezsFFiG3qF/Vo/kk5ftuqdBaQzj8hIQoY/Nd+pPu4ROZhsJNrpFMiCNQYR9SPHzU7qNCQ5SZBkOGKAex6/4lclmzuQAsXn5zppsFMTcOfE3We//slOlpd5CkX6NzNlNvaSUNg6k6fihep2dIjsHrRNXfPI26Yyat9v3ixahKREvPwPMfD4hhMHzmWmpt1hoUoFwCKqNfuiLJhLvDyfJBy6yDCsail9AXscVP1uKqY5tJKGF/8B4fuvVyYE1HTizb36v/iLN+KXzxiv20pZ9jQxwvh8iSg8pfgFI8Ytk/LqRl70Tzq2/WJYZvxGjav+Sst+0M515I5YvAz/uN6j+3d5ubPZ/E8H+b2L99e/umxjPO+oMWyIZhe25T9Ei6CHbz1B/RP1RDpDil4B37FnRNzbYJF9BiibOdq5lXP21LB2XzKxEkh2e5Iwf2/6r8P58UfcP3w2H+xu21fb2EqfZoFwQ998FzJ0DPP8EhIxfMQbl9sTWXCfSrxhbez2wiQfGR5ywgVI+RcFKeEpCDaUpv+aGFD/rkOKXAFv/k9HXP7NNzpIUCxs2Iu5OxKUMVeqt11KDeLt9D9j8czE3JfNeZ12w3pMvMnS0Q4bpx1WlpTV0oSY323v/7GmTj7d86Frd97cQAE8b4DRs/BLCs6HDE26WROEoi5xhHJabfX4HuQ1eDj8DoQYpfjo04xfJVtvVX4vqCGyfhLHJFmAbfCr6nnTHFBdO8b2uE70EbgUGYNSYvp9b1Dpqb5c94B1ycra0SfGzjlixPHwFECkbLrRs/BI2KyW2j5IM6o2+SaIAt01slz3AhgzNNc9EyPWQdciqW4VPbwn2ha8AC+fFh2nUr6Ko54p6VNvLhixhFkTHeTRLGCjTyHWnJAPrPwDe364AVq4AG7dW362sZT5sZLb4RDq8ilyUK13FqCzdVlnkdJ3hI4Eli4uWIpr2j4uWoOFp8M8ns1QOPRXel3YtWgyHiOiFrXcmSWtZktXZlHJegJLPRq8BtubawYsZz6FkJbHvaigkvjGYzpFtccFTJw9LolGVdcZP9j2+N8uuHAC84yfB2+c31vMh3KWkrcgWkjM+TbjSG23PF3Ft/IRMuYiulGn+pA5UeuecvDyxrLl2rA2oFZKebczY/OQg9NGpd8orvfYVOLbdztbziM+8uKwbBbbuhkDFt9hXlVy5IF+QDQMpfgEtTrJXSbPNaUQiiyZ4kf3iD2DjxmfKhv/lUA05eu5lW+pVWen1jjoj3xm/JH13g02ALbfNTxYiO7b6kAS/lGLhAsl4aXmoiWSWkmp+rpkArbdx399rrSMXh2yBGwZS/DT0PqKbUHl5X9wlexnOfjclTw/sy9+sOR1eM9RhyW4sYXFuXxQ0P5b30UrJz+btf0ROchB6hNZardWd+LVe8dgM9WhRFLm5zTUFqqSw0WvAO2ESvBMmgfX4NE0jhxk/tn2Bs8k+nD633QCk+Ol0Yk241Msc8UPFGAMbMQrepGvgnXR+cJZPdlDwK4hlGUjS5CzLcxDdCDvvLCnNxI8qBVma0atBA8ImbAg2YUP5CHkofhMPsp5Hqgzb7AjvR78uWgyrkOIXgJZ6Y4kaUKL6/5wUEDZoCFj4qDHZXb0BGXV39TLk+gVAel08ZTD4D9ctW8pTkkNyvx/BunglUfzK2g7KUr5J5ODova5PT2NYgm9KXXS8Q5SMxn/CVHyNjGZNlGADBybf3//InCTpyVDyyDYTioJzVcU5gXLE0YEyQGiGT1Zk1Ufz1W225ba102yGDgfGf6LmwiMuGmPAWp+QlKkM5a3BJlsULYHbuOhfr1/MqVJZaIJNLKT4+Tsx2bEzsd/rSyTxiKSyobFjts7tiG2kbfxiwqnO+OU6AKYt9eYjBaFJ3Ywfh9RLy1DH2JCh8CZdC++My8H69Qf7xv8lhvcOPhHsS7vAO+ZMazJlx15FZ59yRPFzVbEekPyhXwg2JmtcLX+DkOKn9Y7lIrG1czzZwRlytp8LrNpqKH66IuY9O5yaX47yDHbIyz9Qjo66TkQ7Mov57YHfbPCQvpn5wYMT47KWVng//0Ntl3hiJiUo7zAyR5IZgu20e8TF8n+ZeTtHPFcDIprAlKvpFT/v81+qnWHZ0gqsJemDrowdXxHk3tnJbu7Q2BASlZVDu3oJ1xHJ9nef295MNu+/HX/PhInDhA0hytj/SbVzU47dLaZdEOzXhwEDkz8cGgZa6m182OAhGH/VPej/l4vBKpI2YqWwKTKM1mxaHjN+GvnFDYBl3tyhW9atOS/Hl4Gxaybe1nL1ELnU60uz3wCwvfaNiqieVxwG2qP3q0MA/6ziwEGZ01RC+0NNIp6x/qrcSl48Do57Noq6jB82ijS94gcA3qDBYP37y0dQqRfrrKcqTrnJW/EL5C0Zrr/fIFhBxjV8J2S4ttSrIY53xGnwjj1LQxb1KKVi8JDE2+zzO2gkGlb8ItLdZHONdBXIMuM3bAS8P00GGz8heKxYWXZAxrQfts2O+eSl1GYaX/EgiqUkrdYxFL4IvN8fa1GQPMnjvKmsyOXHAn78/HeS3ysbMkw5r1RZ9t7PSDo68rBPbg42xDF7PUmsbpxKU5BWLldPUwB1roNCXoXYRpuqpxudUTRZPlZGjgZbO8IUpsS2a97vjgbb88fmEy5xmcTCQg7IiVJDip9lWMqykfP07ORyVu/TmWGM29WbEs3CKS/eLt+WDEmdrh+rs2Mps1hiSYd6mtp+/MLH46R02V6CuYrsrncVkvJziai+YcsvBJV8U0b9kf2Qih8/M2KYRvhnem2itEpmo19s/L6WFD8tHG2ZhvG+uge8U/6hFimw1Jtv9WIaLmfqZmHkM8vXFsSWjV9pMfe83qRrwL72HYWsdfIO1ZWwT7S4NEeOBj7pU3JHjoF33Nnxou3wtfh7rZJnsgJg3947Z7s4SbTzq4/HKhWgs0+JF08+rJl2el5WlO48YQxYMDefrMZLbrIktCHFT4fm0Pvg7bArWI9dm85sWh59nVYecbOEqps78iQ5P2mlt1Ew+Lxs2AgzO72TEMF0xUPTpKIxxuAd/pfgtfU2BuLOV02yT1xH3r2Ut+c+8M691i9ITMCyzPjFXPefRtGpMZMrmVfQTKQcsF8fFryQw8kdyjRbv2cIUvy08CkI638y36w3+nR+ecUdbZYYx/93RPUybVPWpeFNPtbGLw0NZ9+moA7OLn4Fxsa5yKHZYfHmTOn2FVDqU7NOCqAmt5RNa2k2d8TI6el++HVHP+gEubzGromRvzwYlR/8MvEEFZcIerlgYJ/dtjBZcqUJutqStFrH8HXi3k9+ZyZN6WXRPGulRl4pg6J31lVg3/iupjymiBlwlfr9JugdXMa0IqxklmBgqVc7nRTM6X3xyXzze30/PA/eIafkp8xk8bsZed1v46eR7ic3i0gz2sZvxPcmorLb9+pvhe3AXXEnEn6OMePyyjinfJoXUvx08LfL0Hm1uv2Sd3y83U585pbRmPBL22zB+vfP3favXoi4hymxjV+zYXMGNCVtPRO/nOpKUtvK0u78zfrbewdvbfpZePv8Rj/tbrwTzs2cRhzM2oxl1LpuijuXcF34xAYZZbBFn9CMIT87RerrrEOKnxaS2zuVHQK7jM7sX0z1culR/Z102vsKn+vs0lm9eVLwsjPb+VsotDx0FCjtXb2S6fVga8bP98zMf2brqDHdF7O/DzZhAyDVpY3ujF/slF/fnzrvJFLJU1xS7ylD1wh8+JM7l0aCFL80es7bHd3Sdy2gAJhaP3GxUWWUKTZ60c8at9TryBJLFDnXD5Z0LmfBM7bsh7+0kGjsDzOkKn6a7l2U7sfcG7eWRLbBuGzv/YEJG8L75SHpcS0i7c8xrv34L68luev501vGJJCUV4ItoZN9fwR5yVmW8igx/YoWwHW8P/4J4tEZYNvtFB0gqZKqVGAX67rWcWj+v+Nm/Ap+WCPZM+S7vTvnMhuQcBRXwQb9rF9/CNPFkUdbDefx4XvZ0wiTtBwXEdc743Jg+Ij0fENhvF32AHbZw594ehoA2A9/BTF1SkKIlDZl6jv763vVZce+shvEW69JRE7ZbJP2jsLfADPuTA5QGKEPZMV+2zvnGvBDo44hVMk3LaiLA6f70IxfCmzUGvC+9UOwNWIMWy1XvLpzQW3NSg0fCe8PfwplrvNsMjsQ7ZWZ95eLa8bmScR13KGy9Q49VS6NPMi7f0tSIIpw4bHmOmBf2sXnw8645hf5Z2pYWcLt9r1Z9b78jCC5O7jn2pgWsMARhqH7e+0LjJ9gcBNbyn3V/k2nHX5yc7DvTqz93do328k2/JR6npFLvRHDqopJiSvUbe4YGx0ujkGDzclCGIUUPx24pof3wUOB2M4lugPzfvRrvbwUYL88BN7frgDb/HNgPzvId0NlMIyKk7+NHxu3Vs3Y/OCTJCPoLcE0nd88H5E7E20zdk14P/8D2Hob134XuLkDa31CI1EF+9EsGF6G9771Q1ROngzW0pqSr2yKMQE33UpFLF9ysisRvo0Km2wO1q+22MWGDIN3/Dnwjj0bTGbJuy7PqPxLotilEZ7wGzQY3qkXgn1pF/X4uvlK4P1psmZGzQspfllJ6njCnfmoMfB+d4xdeWTpsV3spqcj9MvMUju4CGSUxRyUJrbF1vAuuCHubszfIXxLmgM3/5wRubTJW9FMOvI1b9+VkULklN7goN9J7/DT6mc+ZHxThhw4Z0Znc4cL+MtgYN+MEPvsF3QTzByOrbsR2PobW8gzLkpC41p3I6f1Rrbm2sCEDW3nUn/pc9vHBGW1M6QHGpxdbIKPelL8tPAbhigUIWOFKkN+vL338+Xtu2F044rETjqbVPqnh0kSxTfoj/jJ/vX3h0nYRukwfkJN6Qyk71Bn5ETHaFOGvrS90y8L3gmfEbzexuh/5pXpSabuGJcVLeW5C3s3GgqY/5SRnsupM5+Wn09meTL141ZfRrbm2tpxMxFZ7hof/rHxs+H97OCUEA5ryw5Cip8Okt5c6jrhxC3xeduMxeQnYpRanQFFZiedTWSKetx44BPrA6PGgG37lWCwT6wPttPu8Hb4GgZusXV9Ml/bC9h4U+PLa2yXPeCdf2NwQ5FEmXlnXyW/xJ1GwXZIbMevwzsw4lSE3gA5LfUOGBhx3/f3wEFgScek9WK6PONm/FxQyhPwi+dlVSw0oyW8Cu/oM9PjBzY3aSi8SVXBpfeXVRZtZ9sR8SolORqwJNCuXi30/Pix7b/qVsOOIm7GL0ZuNvFAiKsmg335m9JxtI5Z0yJd82OeB+/4cwDOwfrVzxB6P/ktKpVKbdk7/D4HD0HlyNPBp90M8a+rjMrNBg6EUPUHNmIUsMXW8E75O+BVwI//rUGZ8oV9cRdgTEtCAMMZxg3Ghtorv/hMYFmnhgCqFNS/RO0Y3mZH8KcfSQgX0VekfXDU5eP7vdGmwKyZMfGSk+0NtvYEuYCZSLKjYHYmrwYOBtv9+2CbxZispLmgYZKKqwm0lHmTQjk+RhuAFD8NxPNP9f2QGBi8v1wE8c6bYJ/fAVi2NDpQ7nVNZsZP4tl2/DrEZp8DRq9Rn25MfBEeDGwhOWgzr+LkYfNs1+9APDgNWHNtoCLfVJnW5gMFcvt4SZxON5yXQr3Xef7Z76RkLzlw9SpIMffzOl1Bgsp39qlX/OL6BwNis69+C9hwE4h/3xohjK/9hI9IU85I4uM2KU7Su2YeIKp6ciUxcCC83X+gH9/wSi/bbCuIV54zKAAt9apAS706LJrf97eEHz82bjy8L3yldui1O/2yD98M2FY+I+ukGRd/7DEtfRtBAh/0MQ9bNdixRS3F9eYfd93Jl9BHT70Z0wLvrH/CO+as/HcRDxsefy8PWapdUm3LGH5l2f8RkCWbT26eHsY4Rc34RVyL+piKFc/MsiD7zLbR4fr1B/vp78F2/Q7Ydl+JDiOfaeSfiTFk66uqyxSbxIqc0dn4GuPQ/7A/62QcETTlQ8gEhbRju5DilxXVAcgVp8Zx5nej1kD/c67G2jfcH1z6zOrTz49JB8CfWF89f82iFgXYvbEhQ2vnG+dNv3615dZIXFCczcrAPr8D2Ld/DPbrw0L2RBnyiTAdSBckY9tw6aMmst765Jvf3vd3z8d06lJv0m9Ws7mNwfvyN+H96Fe1GX4ZttouJqEU20SNySn2g18An90O7JvfK8a+NipPHa8OccSVZRRpZx3L3VAjYZWEtY43k4dDkOKXFdV6Z7sCG4CNGgNPxpt/ZGSNZRBbyJzPScQzao3o67kUH8t1pZd5Hrw9fwzvCzKzQbLTPAoC9Ay8WZdqVdrcYAk3NPIZR1ySvAZAvPi/7j9UlR7fagVTmFmTSXnQYGCDTRLzTJMpmvpn9L7+XVQOOBZsYMIKRu6Yq4tskG8DVNIqTShe4jUAvWWZUVn2/nSBL6/uf3b/ITBhQ7Dv/jRT2i5Cil9WlL/QXVE6jGms8eHyeFRZJ8syvtb0hTCcnCN1JK+l8jgfXYlGQooyjBytFl43n0Bcje5V1r7QwLvxTpgkHVYLpZkbSfwKRA7txDvitHqbwDRzFofsLI2hUdZhJZztsXfthKj9DjclFfpeRsZZ0qjNSd/dF5UTJoENH5ktbQehzR2ZUV3qNbv8mBta8uWw1CtLYCO2bmHntATjiuIXi8YgsP+REA/cBbwZtesyrk0YLIc11wEWL1SOljqDZNwOMS2/tLYjmWfr2mBZNzkEso3KV/ZaXHzf7Z2/BQwdFnI8z6yuMLCtv1Qzd2ldG5g7JzqM9HP7cPHItsilXoWwEnjf2Qdizx9rzsra7SOa7SQmUvyyEq4v/qWB1ghnnFmXeo0d7WSposskW0gjc7CzLRDvt0elBzL4keJtswPEZz4PfuAPJdMr2JGviaxV6rnsUm/vR5Mv3OafA15+Vi3PPIov6lli61SyQN4+vzEgkBzeXy6uKXqbfrb7Sg7nB/spQjEcG3UkX5yNn0++Sj+5M6d7NqzFlc3AQWCpTpoTMFhkzPlZmOzQUm9WwtPZY8bCO+l8eKf8A2zosIjwDhR52G5FpqOS7swsfIEn7HRjX92j9ke/lG8YF7+yY7Hf8bCtv5Qltv14Qsjb+Mk4dy3iY0PL6XlM/9Btb8m+mXJOsmyexpuDxsxXIGh3WNV2anIDQk8q49YC22yrPiVFQybvqNMTAiSnJ2QUKcOw3X8IfDa0ASOuLvE++b0jTgPba99smQ8ZCu/86+Fts0OCgIrXc8L73dHFCqAJzfhlJaJxsHXWS4ognU4kGRUY9o3/A9t1T2Be9NJFZqSew6AB9ud3ABu1BpC288rIUq9hBgyIvu6EeCzhPFhNAWMnEFn9UChEfIRQYuwruwGMQcy4U0+uLBha6hU9JRATxzt5MjBnNhB1TvKcD7XytMaIUWg55CQsjlJiU2f8FPq3cFK2Hj0kUmR9DQRgYBttWntX77yRml4dUe6uRrfAO/ZMiKcegbjp8pQE1GGDBqNywLGoHv5zYPGClNC+B1hjLLxv/RBix68BnUvATzpQL/+ACyWdF1mAx4Vtvwz2uS/mnq8JHJh+KjuqNn4KQb/8DbW0JfC+/3OwUWM0Yrpnl1FLioFtvGnt1IpETJ5BbAbvqL8VLUIv7Pu/qL9oXPFTrA+ySlW/frWzp5MOj9dZdo1PTC4drRm/6DhsyFCwDTaJXiqb15YtTxP4X8dPf4/B2+6o1mXoboTJ43kFVwufNlMYvh5yJRKnTLBRa6SvbJgkULQ+mQPydy/hjhgNNj7h1BOl11QWu0k3xhEdSPHLirIfP0m7qZFj4P30AC2RJAWxEzzfCT95HFvq9X53DNiEDWLu5l9A7Ot71V80XmYKCp6KP7e0sJWK5V3dMSgpm2px+o5IDHbhbhipd8ug4pajd8JPpc4xBCtC0oeCQrJppJ2io7hEzPb4UfD3F74C77dHwzv1H/WBdZukVluOKTTF050S00q9l5aPhfruRBuyCyl+WVGuI5IVOMZuKU8jZz1Y5J+xYXJD8nxl2SSykrSzuYCOJ0phYBtvljFRD2z7ncF+dWj3b5XIoaXeuoFLXqnwzrlG3mmvSZTeY89Sr1yXzPb4EdivDoX310vU5bKB/1k9HcWv+7kV9T7pIs7SdsNndP/fRGDIsHg72dR3GEqvf9Dkg3ke2NZfBFtzHd/F6LhWiStcna5UpS2UY6W31JDipwHb4Wu+H4oDik6l9n1hsvU2hnfedRqJhOVQFUR2eUuiShXxRWVA74tFJz1/p24iPRts/rk+pc2P7PsbNBjeLw+Bt91OyeF0dg8r1CFmerZPdlZOZfmyR7mQTbt/f3jb7QS2xji1UxHyoOe5IxW/lJOLlJZVQ+knll0GzSCs+K0xFt7ZV4H95sjo8D0f7bFLvfqiaM/Cm9ztHXhHOXVWsfMlGrahBCl+OrDtv9r3o78hmwulgSxit7BtZOWr+KoUN3MmL0tTHGTIaanXO/y09DC/OwZszQhXP47BGItU2hgYWidfD+/rewHrbqSSonxQgWCdq6t/rmjHCRi08UuMonM0nHEillwjN3eYzDI05Zeo95lt/6xfv/hl9d7Z/Lg863aL2Ef2+WNFjrHxc8FLBeCcKY/rOPLWygX75GbwDj4J3knnKS8hudFJ26P3fNchQ4EBg2ICqfV0bI+9wSYeGG3zIo2JpV6JzqVlXPL94SPBYk+q6KEYpcY78HifCMkyDFh/Y/Tbez9gdMyxbgDqBzgFO53wrt5w2Ufaf5sqNwX7wsQNKCozfhLplYUsJ1dwhRk/1vu/dLIoBqqbO9Jm/OrQMAnIg7iNRptu1fdj0OBMaQEAVq2USUAuH0IKcueiCdtia7sZ5Hm6hUE/fqylFd5Z/6w55Ix7BlXFr39/sB2/rhSnjty+CFOebcnifMQAaoqHyqDldzhuorxklLXY62nKl18pjLjmAjriuDKDogqL+BHZ/n0BBw8Fli8NhlVd6o3Il008EOKqycGgmRQ/hbhjWsC+sJO59HrpWQrXiAroLfXGKX7rbwzvsD/XPmL7S05ktEQ5iO6ma7VcGq4hq/Q6SEl7mQYk3MhsD2IW02cjR9cOOHeJzX2Kus0j20wUq6F34514Lth2O4HtuU+u+SZkEH9np92CF9Lc+EltIsoDQzN+KX78SoXsjJ/frVSP4scVNJu4zTw7fh3eGSFfdzkofmzrL8H7yyVgA7tXOiRXetVmhjWfQ2upN6G9fuozYIHj87qv/+IPQKUCtu/va7/3+S3YdjuDfe078rKOi/DJqnniixZpafacQrLj15LDOQzN+LmKY1/+4smHihZBC++ESRCz3wMbMAD8hadqF62Ore4M3Gyd9cB+dSjEc0+oTxSY6FDrBrj4zpv9eH+IB6eFb8SnrSpeyFdaNgwayofJskzqCj19V9qqhX92r8dkRnVZNeakIDamJZRXDjN+ngfm97Mn+ywqG17XWc/pbQzeF3eB2GbH3p3K3s67AzvvrpQG++q3gDmzIR6Z3nctzw+ilHrrnXUl0LE45aAGt3FLu2hmGIN38Il9P3f/ge+enfyU6OywIIR92IQN4X3xqyFF2mInUoIZG+/QU6NvyJpLSQ89od2QcWVTrdZsZQMujERKWSZt/IgIHeWnMA5j52HrOCUuaZccWWRR78UX0GfPx8atBQDwus9rlT5SMKcTj8yiLwvb9LNgP/kdvAOOVYwoaaoTu0NcXuawexpVWL/+8CYeCHxmm0zpaBMxk+mHjRhdaqUPoBk/p2BbfB7e+TfUjmZab+OixWkwTNisSYRxUO8TC+b2/s1+eQjYp7fMKeOIAttsK+CV54LBnn4E2P8IJG7mkGHBvNhbbOCg2g7k92app5uEpG6aShbFxImPjQinvilysS0+33vMHvvOT2r/brI5vEnXAEOHQ+yftjwYbeOXKp8quu9GdqOKoo21FzaLkEF2ufp7PwPGjAXbbCtg+TL1fCzCfvgriGk3B+2kTZ4C9ctDgLdfB9tVYVm6pJDi5xhs8JDo8zhTIyoa8hepoRTix8/ikW2m0zO9sWf2u31/+891rSOH9yJ7DFudjV/SDuHuv9POGDVVrtKG8hr55bmpyyRR7j4iT2Tx/f2J9eEd/Tdg+Ihav9cNGzaK6g+FAAAgAElEQVRCTwZb1fft1yXzDwkge2SbjOA5zViyQYPBdvteLcvXX84lz3hhguXife07ELvuCZ76QaCHt/3OwPY7W0nbNUrayxB1bLZVepg4clfE8lf88lvpyfhsa4wD23JbM6L04H94aRuyhHDShSkZrsc9REC2lN0dWjtmpdeyzZCTHz8n8G/mWtE9U5T6LAxsw0+BRRnzSxGqI0WXXfdydR+SFSkPscv4QRHRzzDGgE22KECYxqKEtYGIRLnTK9DmpWgDdqu7erM9m/eXi8EGDMyURj2SPgz9He3i+ZJJJ5RJxC22zY71F3ts+yb4nEFv9OnoWb3en31dF5O1BRo6XC6cCrIzmNLpqXfJbK99gX79gbyW8KPw+ScV1e6VB0uKGNvl20ClAu+A44J5FKT4sZ/8Dmzbr4B98/vBG9I+Cc2508qUR1IUh0wkvYP6bOELV/ZLCil+RdCz3X9gjIPjME4ZJhugEAP2nMowYz/EYs5oNkaSfEsW9f4pnv6vney32xns2z+OvumbNWJ1Slr9RhHvlH/AO+QUsE02l8rb+8lvo11F1JFcV6R3GKoMSt1tnK27oXycnmzGrglv0tXw/niyROBa22Nf/oZyPon4jpVEtauWhy3F70e/hnfutRG2qsUoAd5Ou8Hb7zCwgaEPNpf67QbSj+rKmVCGbPwKwPvb5cAH70J88BbEjVPSI6j4tiLSabavxMCEX4LSHQhnQLmJWqrxPOBz20Hceb1EGimbA9ZaB1gr4czjcPg1xqHyl4tQPfrXwPz2+IBVM0cNail+P94f8CoJuytjsho0JD0QAO+MKbUNLlt8Xin9VPwfLElHNf5/e3ceJkV19wv8e6pn7VlYZgWVYUQWAQe8KoIvalBUSCIIQbYLKA5g4najqMTlEpZrNOAbrzGKJOTR+7BcRPIa0OhVITrX5CK+PlFhVJRFMEhgZhCGYQZm6T73j2F6unqtmq6lq+v7eR4fe6iqU6d+ferUr2s7qnttu7YqIQQQaXtjxDvuS5XNoDXxKygytx4AEv9Fakwt9K3TZf20hXjGzwbCm9t+lkJoO7sjps1r/9B/sFk1MqncJJJMv76Tle53qBkk2kDrZjXLeG3h3BkrbYy91Cty86FU3q9hWL+uET0KIIZfafyZ5eDyjEqc9YoV7kTugS4q7dpyGvocMftuCEPfMRnFeX0SWz5pu08XHLtMwDN+ttL4iP11P4LoP1jX2Y2kZvdTvS7rLOTJzvv15BefAjdPj7+Q1tsQYq85yr+r4y86hooLPbNt1z1bvS6A3PelAQW56He1ojXxM3g/VJ1BjBLv3n0Su+ysczz2wGLzH4T/6UchrhobfR6jL7kD7a/GSU+HGFQB1B6Ff2dV+20O+ksyvG4R9RsU+d/5Y900hiV+ra2teOmll1BdXY2TJ08iJycHV111FaZNm4aMjMRe6Oh2Qgigz4V2V8M4dp/CN3P1ydhXNTV2fj4Z46GNoPu0NL/LSufDHZGIQCLahddcdIGYPAdyzb9Hn/6T24DmZqAiwcuhdrdzK3UvCHxULH3IxIIdrosJiOg3CMoz6w36EaVDdg6U63/c/rnsInguH23t+jUqeGwlvn/zPyCmz9e9rBg1BvI/P4By+70m1Cz1GZb4+Xw+5OXlYdGiRejVqxeOHz+Op59+Gm1tbZg7d65Rq6EYxNgJkNu2apuZv6b0ixqzBF88bDJxXh/Ig3vbP18R4yAQPJJCdox7xjQnNFFiETy01pS5gXGdRdlF7WckTaZceS18sRK/nDyI+Qu1FWZUcpeE7UYPkZkJ5YnVwNkmiNLzos9o5maaNp5rAiNt2DJmuTPakveqMWjoNxi+LtwaIOb+HGLmnbHvbb3kcmD3xwnUMHUZlvhlZWVhxozOp/WKioowZswYbN++XVc5DQ0NaGhoAAD07t0bHpOfcuwo3+z1RCQUdDR5j+LRdd9N6K4ihED6zDvREifx69hOv6Kg4zCvKIpq+yPFJNKuqSVmoct5PB74FUVX12TId6N0xlrxeKBEKTO4vqFxEBCBeqvqFFS2x+NBvDvlQrcn0joNVXJeYB1p46dEbWfy/LJA3T0XDQrMF+k77Pi/iPNdRtoeddsTgXmUCTPga22FcvElUDweSOkPzCcg4sYm2uEj4nLzF8K35X8jrfL+qG0hUrmekJgIEf07ixcb1bwi/vYlvXO3osTqUxVFdO6HIf1OKC37haothezXge8oxvJa+ELOPHelrGgx0dymI7SP4Pik/+r3aH10AQBAEbHjqpXfExRbIeLuJ3poOe76RZT+tkNa55P/kfZ9Ae0psJ6+xYz91Oo8xNR7/Kqrq9Gnj76bSt966y1s3rwZALBp0yaUlJSYUbUwhYWF8WcyWEN+HjpeoFFYWIA0Hdv6z5C/MzMzUVRSEvbvoTri2XyiFh3PNXbv3g3eCOsOjkmkcrV8N6HLlZSUoLZ7T5yNu6S+9cTTmJ+PjnEdCnoWICNKmcH1DV2v4ulM8IKnncrLQ8cgQkVFRTgSpy6h5cZapxFO5eR01q+4GJ68btEqhtZVr0L6fcjo2/lOvdDvsEePHgDa20ddZibORFuxUCJuT+vZ0zh67nNubh7yg+e5/78HPsq2Nhw+97mjfccSre1HjOktM9r/0yDS99Pxb5kZmSgsLIy4bq/Xi9Oa1gBkZmbE3T6niRSX/Px8nDj3uVv3bsiJsc1a9osz3bqjY6C+HgUFyAqar2P5tLS0hParf6V5EPy4TyJlhcYkXlkd83o84dsQXE5BYWFgn8rLy0WeAW2pufZI4BjRrXv3iMeIRMU67tZmZgaOE1rjFCwzM0PTcSZ/eiW66SjfzJzEqjxEU+L3/PPPo6qqKur0yZMnY/p09Q3jf/nLX7Bnzx489dRTuio0fvx4jB7deTnq2LFjupbXy+PxtB/A6uq6dMo5Eb5TDYHPdXXHIaA921cuHw3/x53vWmtubtYUq455/N933utVX1+PhqBltcZE93eT3x3Hjh1Dq66nJo1pA776zvEdj39/HIqOWHXEw+/zh00DAF9D5/dYW9s5Lm68cvVO6yrf6c70o7a2FqIpRneY6e2oSNRZTpysR3EfoK6uDmebm2OsWUbcHv/xzrZ3+nQDzkRZlwxqe1rbdyRGxjS0rObTp1BXF3mM4KZG7WOdNp/t+vYlm+D+I9Sp+lOBz/X1p3Ba4zZHi43/ZOe7J0+cOBFxv25ra0sotm1t6j6wK2VFi4nWsny+2NtwPGifamhoQJMBbcl/4kTgc/3Jk6pjRKK0HGNag/qWrsS8ublF03xnbpiEszrKN2M/1RIPIxNOTYlfZWUlZs+eHXV6ZsgLFd944w1s2bIFixcv1p3B5uXlIS8v6BSuRcmYz+ezPPHzB70+w+f3QehYvxwwBAhK/KSUmurfMU/wQdXv96v+Dp43Vpl646U8/gx8Pp/u25mM+F78/vjbG2+9MujCQfA01ffYhXK1Tuuq4G33+f262lmY/oPhPzc0lc/ng4w1OkH/IRG3J3gZv5RRnwIN/o6k9Hc5NkbGNKxN7NkdtXyp4/U4Etr2XyeJtD3B+4rf79f86peoMVb1YzLifi1jLK9JyPeYSFmhy+opK2a/4e9aXGORIWVq6TP1inWMkTJyf6uVlH6IkWMgP3wv5nx+zSOsdL0uesq2oh/QlPhlZWUhK0vbk0mbN2/Gtm3bsHTpUvTu3dUxGCnViJl3QvQoiD+jJVz0tCUQcqNLgkPKPfSk5ldjKHfcn9C6HPE1pfONBbo4/CEWchApIe74edzEz40Mvcdv7dq12LFjB5YsWYLS0i6+9NK1nHCU009Z9jzkof0QI4LGaLXlPX4mlp2d0/k5GW/QV73rLLHYhyV9UcoTI38A0b1ntEK0rk17xWwiho0wpiC3JEQGjNxBEZjepzqwfUrzhg10OsMSv9raWrz++utIS0vDQw89FPj3oqIi/OY3vzFqNakloc5e2wHYbqLXBda8mT5ePcr6dXZdPY29gVaMGgP50f+FKCqFiPbghI3EJZd1DpGWYdU4lwa0x+A2naTtOyY9+7cDj6uJM+I7dUkiqav9G9SYHB9PV+5UmhiW+BUVFWHTpk1GFUdW0tKpnN8XOHzQkNUp434C/6c7tc1sUCIleveB8t9+CWRkGp6cibR0eBb+D0PLNJIoHwDloV8Bed2NH6or6kq7PNFZnJiQ2snoM5tabmNww3fkhm0kw7hoTKEkZOfOqnMIM80jOWgg+g2CmFYZf8byAVAeWGbceodeBjFgqGHlOYkYMBTCjCH/tLzUuovcdZmGZycoAcH7CptSOy0/MnoWmV+PJMTEz0623tej8xKJwcdg0SP+Dud59GmI88uNXTFZQ4nRYETUP2JI0iQwVnLqlvv2dDExJq76oWAxBzdlMWNBjInubDNM/JwqtL3qbcAGPulJLha13cVqU8HTHHxEARB7O52+bSYz5KCbvGP1WktE/EiAGDzc7iokHSZ+bnFBeft9Xh0MfNKza5zQmVKXufSXtIqeJu6I5MIARm+mW+IWjym/pezdh0WfCxMrINA22BeFMnXINtIh0bYZ50DrWfxsyL/ovNTL/hXKgCHw1x6NP6ObRDvwxmqPqmkmd8renPjzJILHFF1E9wIbuhI3fEmpt41i/BTgTBPEgCFdK6Cjb+KP0DBM/OyUEfTyV5HoyVeHXep14C91z4wFkFle497dlsqM7my7Ut7wkVBunWtsPULFrJfz2riZlPuXAtm5hpapfkbNpH4sGfoqPduWDPU1gMjIhJg+P4ECzsWMeV8YXuq1kRh1HXBBOTB8ZOKjWnS08XM7ivivP9WxrPV7hhP7JuHNhTJ9PsTFw+yuStIQ/+WqaFNiLGRKVcIot8yCODe8HNms4gqIwZd2bdkesd67qeGWFScf+PsPBgAot95hc0Uc2GEHxGgAlr3XNLnwjJ+NRHpGhEuwmpcO+bP9b+X6myFHjYHw5sK3/sUYy+t7nYsJN+cYXB7ZQYy8FvI//hdQf0I9IdZTvVaxpA5JsJ1JTEyeA/mfH0AJ/BDVfm+xmHMP5IfvJT70n4Mp9y8HTtRCFNsw/KnTL5EGzvhF2Q5vLpR5C62rTxLhGT/HCkmcghq38Gq4nGL30ElOPOWn1aAKu2tgGaF4IC4fHT4h5hi2FjU4Kw5cMa/0yvaXhruYMn4KPIufheh4X5qO/V65+kZ4HnoSoqDYpNolP5Geri3pc3qSZgPlmbWJP0DiUEz8UoTQfY+fzU/1pnDip9znsoN9pCQvGQ5ECd83q2klsacOvUxbMSm8P5hK54vo3YFtSSXaeOJKEo6rbhEmfqkioQMtO8wAA4ZzE+npUH6zDoh6/1uKKSrRN79VY/Ba0ayzsqJP4+tcrBX1lZJu69/ctr1xuO77j4/3+KUK3S9wdsng5jopDz0J+ZdXIMZOSKgckZcP0bPIxb+9k6FRmVcHcc1NkHt2Qdw8M8Zc7v32owp6UEP0viDx8qxImJ2QlJtdRyfEIKpk6IuSCxM/xwppzFnZOpfXeYnE0Tu+dqLX+RBG3fCrtb/JzQdOnwLOKzNmvUnBhvZSWALUHev8WzHvgoYy++74M7lkn9FD9CiAcvdjkC3NHI6RzMXXuUTFxC8VeDwQt8zSt0yC778SCx7WvYx6/S44KGrcROXRpyF3/BXi6pvMrY+TaWmjofMYnfilVGJuHzH8SnOOxW6+pOfmbY+LsQnFe/xSgLLseYj87voWSuBSr/L4M1CuiPAkp74KJLh86hBFpVAmzEz8XY52ifhVah25wzjKHT8PWY8x3Zty92PA0Mug3PWIIeWRWaLcxB/pqXNdHNBXpaV3fjZ7tBqHCLxon0lxGJ7xSwVd6pd0LmT1U8Dn9zV/HWZzS38TfNAJsOFgeeEg9d8GtVMx/Ep4hl9pSFlRueEMuBlixE1Z+jvIA19BjBxjYYXiUx5YDv+qpyAmzDCsTJGXDzHmh5A1/4IY+QPDyu0gpXO6M2Xxs5AH90JcdX37Pzil4hZi4udUiR7UMoKeRszUeX+gEQfUOMc5Zd6Dia+DLCFGXA35so4XkZv1w8HsS73kKKJ3H4jefeyuRhhx8TAo/3M9hMHtU5mpY7SmFCYuKIe4IPj+UWZ+oZj4uVX/we2XQBQFuHCgvmWN2I/ineHgQdsxRMSXNVs/Vq8InceS9/jFoOssHs/4dY0FbydobTW8SEOSvoGXAF/tTrwcDRx9tdTRlTcHEz+XEkJA3NnVBzQMyfzMXwXZKNb3a9VgvWxE7mLS991Q3/k5iW5BURY8CPnOnyFGXGv6upx0qTeMYytuHp5WSQVW3x9kwC8oMWhYvDkSXgc5gJlfs92/9HnfnvksDrHyixXWrjAGkd8DypS5rh12TKXfuft70yKdy+KxJBQTP6ey+mELX1vnZ0/iQ92InoUQ/zY24XIoWcV6qtfE1Q4Y2vnZSbcLMEl0BJEZY6QWso3ys0cgbp4O5ZfPhU+0+wdgEnJQz0hqFo+84fN3fjZqjMOSWIOPp8CBkB2O5UTwpTi77/HT04ZToLnbwu4xxykpiG492l+JVXpepKmW1yfZ2d0zUlepDhRWNGwzOtgY5TjpbI0Wxe1Jrrhhos0VSXHJlAgwmbMWj+8UCdtFGD7c4VgWH+CsPqDafrbGWMoDy4BjR9SXIkkzoXVIwuB26qgfD8wSk5Y3F2g6bXctrKfq5x3cPu3+AZiEnNQzUhDVOJfeXAvWGJz4GdRsYu2PRl1OThZZ2RCDh0NEvPmYohFjJwLFvSCm3K5tARl0S4LdPx54357pRGZm5x8ek/YtvaMipYqUab9M/ELxKORQov9giDn3QOR1g8ixIvELXrkF63DU2Rot2PkE6LhNQZlWCUyr7FrZ/KWf+oaPbD+LnpsHlJ5vd20oGbEbCMPEz8GUq2+0bmVW31PoSYXEjz2O9ZLnHj9Zd1THzKlydsVawuOB56Ff2V2N1FRYEvgokuj9hfp19gNizI+AfoMgyvvbWB/7MfEjbcy4xy9WOXZfpjOExU9ek6qdho3kYbW9X2ifN+J4x0T2Ed16QHlgOdDaAtHrArur03XB/UB6OpQrzX/hdbJj4kcaWZjE9BsE5OabvBKrMfOzhN8ff54kJC67yu4qUDR2/4Cwkbg43ov2HSD460uJEwqJYxRIG1Mu9UYuR3n4KfvP1hgiFbbBYZx4b+iQSyGuHW93LYhSVFA/7MT+wQSMAmlk3b1ThgxgnmxSIpE1g7H3tombZwA5ecCwEYaWayblvl+mZpsnSgaCiV8oXuolbcy4+TzVc6FU3z5DGBsk0aMAytMvm/dqDxMw6SMyUfDtH9zXAPCMH2mlek2GbbVwLsbMMiItPTluFUiF+6OInC64L+jqOxnP3YMrbppkQIXs55yfxZREkuCgSsktZhNxyatLMjLjz+OgS9JETiSysiFumgR59DuI0Td0qQxl3kLgpp8AZf0Mrp09mPiRRhaP1Zty3LStpJUoiTSoPBEZSZkyN6HlRVo6kELv/uOlXtJG8lqvfqkRJ+WB5e1DziXYeRIRkf14xo+0kSa8xy/SmcMLysP/LRUkwz1nXSQuHgbl2Q0QqTZ+chIQY35odxWIyGV4xi+FKfcvA9LSISbMNLZgM5OYlHtxc2rQnfS55Da+mDQ8CS+ChsUiIrICE78UJgYPh/LbjVBunm5IWQGZWQmX116oMcUkLQef5UuYm7ediCiJ8VJvihPpxowBKsoHtN/rldet/UZXs6TsYPUuS4T4vqwUbstE5GRM/EgzS8ZtTKUzRSm0Kbrl5NpdAyJjpFKfRAQmfmQrF3WoKb6pyi9WQH75KVDzL8iaf0H84Ed2V8l+PONHREmIiR8RJUz0GwTRb5Dd1SAiojh4Iw7Zx02XUNy0rXQOz/ilBJ65pRTDxI/INEz2XM3PhIGIkg8TP7JRiidGPMsXWfBT4Tl59tWDSAue8aMUw3v8iCzBJLCD6NYDYuwEyLoaiBHX2F0dEzFhSA38Him1MPEj+3ginHA26uXQlNSUafPsroL5eKYoJShzfw7/rxdBjLjW7qoQGYKJH9lGjBwD+fZrQFEpxHllkP/4f1Cmz7e7WiZhEkDkROLCgVCeWQ9ke+2uCpEhmPiRbURWNpQnVgNCQAgBObUSIqXuiwvaFp79cR9+5ylDeHPsrgKRYZj4ka1E0NBeqZX0kduJiivsrgIRURgmfkREBlIefwby4F6Ifxtrd1WIiMLwdS5EJhEDh3b+4eFvLLcQZf2gXDsOIo3fORElH/ZMRCYRg4dDLHgYomchhMdjd3WIiIiY+BGZSblitN1VICIiCuClXiIiIiKXYOJHRGQH3vdJRDZg4kdEZAe+voiIbMDEj4goQQW/eApQFIjJt9ldFSKimHitgYgoQd6rx+JUWX/409K1L8QnvYnIBjzjR0RkAJGZpXMBXuolIusx8SMisoPC7peIrMeeh4jIDkz8iMgG7HmIiGwgrvyB3VUgIhdi4kdEZAMxaY7dVSAiF2LiR0RkA5GZaXcViMiFmPgRERERuQQTPyIiqxWV2l0DInIpvsCZiMgiymP/Dln1fyB+eKvdVSEil2LiR0RkEdG3P0Tf/nZXg4hcjJd6iYiIiFyCiR8RERGRSzDxIyIiInIJJn5ERERELsHEj4iIiMglTEn8zp49i3vvvRfTp083o3giIucQov3/BcX21oOICCa9zmXDhg0oLi5GbW2tGcUTETmGsuS59nf3jZ1gd1WIiIw/4/fFF1/gyy+/xMSJE40umojIcUTvPlBmLIDgaB1ElAQMPePX3NyM1atX495770Vzc3OXymhoaEBDQwMAoHfv3vB4PEZWMUxH+Wavx0kYEzXGQ43xUGM81BiPcIyJGuOhZnU8NCV+zz//PKqqqqJOnzx5MqZPn44NGzbgsssuw0UXXYTPP/+8SxV66623sHnzZgDApk2bUFJS0qVy9CosLLRkPU7CmKgxHmqMhxrjocZ4hGNM1BgPNavioSnxq6ysxOzZs6NOz8zMxJ49e/DJJ59g5cqVCVVo/PjxGD16dODvY8eOJVRePB6PB4WFhairq4PP5zN1XU7BmKgxHmqMhxrjocZ4hGNM1BgPNS3xMPIkmKbELysrC1lZWTHn2bVrF44fP4677roLANDW1ga/34/Kykr87Gc/w+WXX66pQnl5ecjLywv8bVWj8Pl8bIAhGBM1xkON8VBjPNQYj3CMiRrjoWZVPAy7x+/HP/4xrr/++sDfX3/9NZ599lmsWLFClcgRERERkT0MS/y8Xi+8Xm/g7/z8fABAQUGBUasgIiIiogSYNnLHkCFDsHHjRrOKJyIiIiKdOGQbERERkUsw8SMiIiJyCSZ+RERERC7BxI+IiIjIJZj4EREREbkEEz8iIiIil2DiR0REROQSTPyIiIiIXEJIKaXdlSAiIiIi87n+jN+RI0cwdepUHDlyxO6qJA3GRI3xUGM81BgPNcYjHGOixnioWR0P1yd+RERERG7BxI+IiIjIJTxLlixZYncl7JaRkYEhQ4YgMzPT7qokDcZEjfFQYzzUGA81xiMcY6LGeKhZGQ8+3EFERETkErzUS0REROQSTPyIiIiIXIKJHxEREZFLMPEjIiIicgkmfkREREQuwcSPiIiIyCWY+BERERG5BBM/IiIiIpdIs7sCdvL7/Vi/fj3ef/99tLa2oqKiAgsWLEB+fr7dVUvYunXr8I9//APHjx9HVlYWLr30UsyaNQu5ubmBeaqqqrB582acOHECffr0wbx583DhhRcGpu/fvx9r1qzBP//5T/To0QO33norrrnmmsD0+vp6/OEPf8CuXbuQnp6OMWPGYObMmVCU5P494ff7sXjxYnz99ddYtWoVCgoKALgzHrt27cIrr7yCb7/9FhkZGRg1ahTmzZsHwJ3xOHnyJF566SVUV1fD7/ejb9++uO2229C3b18AqR2Tv//973j77bdx6NAhNDc3Y+PGjarpZm97svXHseJRVVWFd999F9999x0URUG/fv0wa9Ys9OnTJzBPqsUDiN9GOqxbtw5bt27FPffco9rmVItJvHgcPXoUa9euRXV1NQDg/PPPx9KlS5GW1p562RYP6WJ/+tOf5H333SePHj0qGxsb5cqVK+UTTzxhd7UMsX79enngwAHZ2toq6+vr5RNPPCF//etfB6Z/+eWXctasWfLTTz+VLS0t8s9//rOcN2+ebGxslFJK2djYKO+44w752muvyZaWFvnZZ5/JWbNmya+++ipQxrJly+TKlStlY2OjPHr0qLzvvvvka6+9Zvm26rV161a5bNkyeeutt8q6ujoppTvjUV1dLW+77Ta5Y8cO2dLSIpubm+X+/fullO6Mh5RSrly5Ui5btkw2NDTI1tZWuXbtWvnTn/5U+v3+lI/JJ598Ij/44AO5fft2OW3aNNU0K7Y92frjWPF466235GeffSbPnDkjW1pa5IYNG+T8+fNlc3OzlDI14yFl7Jh02Lt3r1y4cKFcsGCBrKqqCvx7KsYkVjzq6+vlggUL5CuvvCIbGxulz+eT+/btkz6fT0ppbzzs/5lpo23btmHixIkoKSmB1+vFrFmz8Omnn6KmpsbuqiVs5syZKC8vR1paGvLz8zFu3Dh8/vnngenbtm3DiBEjMGzYMKSnp2PChAlIS0vDRx99BADYuXMnMjIyMHHiRKSnp6OiogIjRozAtm3bAAA1NTXYvXs3Zs2aBa/Xi5KSEkycOBHvvvuuLdur1ZEjR/DOO+9g9uzZqn93Yzw2bNiAG264ASNHjkR6ejoyMjICZ3DcGA+g/Rf6qFGjkJubi7S0NFx33XU4fvw4GhoaUj4mw4cPx+jRo1FSUhI2zYptT7b+OFY8xo0bh4qKCmRlZSE9PR2TJk3CyZMn8d133wFIzXgAsWMCAK2trVi1ahUWLFgQOKvVIRVjEiseb7zxBgoLCzF16lR4vd7AmeGOs3V2xsO1iV9TU9XUwW4AAAXuSURBVBPq6upUlypKS0uRnZ2Nb7/91saamWP37t0oKysL/H3o0CHVtgshUF5ejkOHDgWml5eXQwgRmCd4+sGDB+H1elFaWqqaXltbi6amJrM3p0v8fj9WrVoV2JGCuS0eZ8+exb59+5Ceno5FixahsrISS5Yswf79+wG4Lx4dJkyYgJ07d+LUqVNoaWnBtm3bMGjQIOTn57s2JoD57cHp/XF1dTUyMzMD2+fWeLz66qsYOnQoBgwYEDbNbTH5/PPPUVpaihUrVmDu3Ll48MEH8cEHHwSm2xkPVyd+AMISgJycnKTvhPX68MMPsX37dtx+++2Bfzt79mzYtnu9Xpw5cwYAcObMmZixibR8Tk5OYNlk9Oabb6J79+648sorw6a5LR6NjY2QUmL79u246667sHr1alRUVODJJ59EY2Oj6+LRYeDAgfD7/Zg3bx7mzJmDnTt34s477wTgvjYSzOxtd3J/fOTIEbz44ouYPXs2srOzAbgzHvv378eOHTswY8aMiNPdFpNTp07hb3/7G6655hqsWbMGc+bMwYsvvog9e/YAsDcerk38OnbQ0AA1NjaGBdLJduzYgdWrV+Phhx9W/TLIysoK2/ampqZAXLKzs2PGJtLyjY2NgWWTzdGjR/HGG2+gsrIy4nS3xaOjTmPGjEFZWRnS0tIwadIk+Hw+fPXVV66LB9B+Rnj58uXo1asXXn75Zaxbtw6TJ0/G4sWLcfLkSVfGpIPZ2+7U/vjw4cNYunQpbr75Ztx4442Bf3dbPNra2vDCCy+gsrISWVlZEedxW0yys7MxYMAAjBw5Eh6PBxUVFRg2bBg+/vjjwHS74uHaxC8nJweFhYU4cOBA4N+OHTuGM2fOqJ7McrL33nsPv//977Fo0SIMHTpUNa2srAzffPNN4G8pJQ4ePBi4HFxWVoaDBw+qlgme3rdvXzQ1NeHYsWOB6d988w2KioqScifcs2cPTp06hYULF6KyshKLFi0CADz44IN4++23XRcPr9eLoqKiiNOEEK6LBwCcPn0aNTU1GDduHLxeL9LS0nD99ddDSom9e/e6MiYdzN52J/bHBw4cwJIlS3DLLbdg4sSJqmlui8f333+Pw4cP47nnnkNlZSUqKytRV1eHNWvW4Le//S0A98Wkb9++qsu4oeyMh2sTPwAYO3YstmzZgpqaGjQ1NWH9+vUYNmwYiouL7a5awt58802sXbsWjz32GAYNGhQ2fezYsdi5cyd2796NtrY2vP7662htbcWIESMAACNGjEBzczO2bt2KtrY27N69Gzt37sTYsWMBAMXFxbjkkkuwbt06NDU1oaamBlu2bMENN9xg6XZqNWrUKDz33HNYsWIFVqxYgUceeQQA8Pjjj+Paa691XTwA4KabbsL777+Pw4cPw+fzYevWrUhPT8fAgQNdGY/8/Hz06tUL77zzDs6ePQufz4e//vWvgY401WPi9/vR0tKCtrY2AEBLSwtaWlogpbRk25OtP44Vjz179mD58uWYPn06xo8fH7ZsKsYDiB6TwsJCvPDCC4H+dcWKFejZsydmzJiBuXPnAkjNmMTbZ/bu3YuPPvoIfr8f1dXV2LVrV1LsM0JKKU2KSdLz+/1Yt24dqqqqkuKdQEaaOnUqPB5P2JNVa9euDXyuqqrCq6++Gngv1/z581WXg/ft24c//vGP+Pbbb9GjRw9MnTrVMe8ki6empgb33HNP2Hv83BQPKSU2bdqEbdu2obW1FeXl5WHvrHNTPID2S3fr1q3D3r170dbWhtLSUkyZMgVXXHEFgNSOyfvvv48XXngh7N9/97vfobi42PRtT7b+OFY8Vq1ahS+++AIZGRmqaY8++iguvvhiAKkXDyB+Gwl29913Y9q0aaptTrWYxIvHjh07sHHjRnz//fcoLi7GlClTMGrUqMB8dsXD1YkfERERkZvY/zOTiIiIiCzBxI+IiIjIJZj4EREREbkEEz8iIiIil2DiR0REROQSTPyIiIiIXIKJHxEREZFLMPEjIiIicgkmfkREREQu8f8Be90VgC6i55AAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(obsds['rmm1'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the SubX RMM1 data for the GMAO-GEOS_V2p1 model from the SubX data server. It is important to note that the SubX data contains weekly initialized forecasts where the `init` day varies by model. SubX data may have all NaNs for initial dates in which a model does not make a forecast, thus we apply `dropna` over the `S=init` dimension when `how=all` data for a given `S=init` is missing. This can be slow, but allows the rest of the calculations to go more quickly. \n", "\n", "Note that we ran the `dropna` operation offline and then uploaded the post-processed SubX dataset to the `climpred-data` repo for the purposes of this demo. This is how you can do this manually:\n", "\n", "```python\n", "url = 'http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.GMAO/.GEOS_V2p1/.hindcast/.RMM/.RMM1/dods/'\n", "fcstds = xr.open_dataset(url, decode_times=False, chunks={'S': 1, 'L': 45}).dropna(dim='S',how='all')\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dimensions: (L: 45, M: 4, S: 510)\n", "Coordinates:\n", " * S (S) float32 14245.0 14250.0 14255.0 ... 20439.0 20444.0 20449.0\n", " * M (M) float32 1.0 2.0 3.0 4.0\n", " * L (L) float32 0.5 1.5 2.5 3.5 4.5 5.5 ... 40.5 41.5 42.5 43.5 44.5\n", "Data variables:\n", " RMM1 (S, M, L) float32 ...\n", "Attributes:\n", " Conventions: IRIDL\n" ] } ], "source": [ "fcstds = climpred.tutorial.load_dataset('GMAO-GEOS-RMM1', decode_times=False)\n", "print(fcstds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SubX data dimensions correspond to the following `climpred` dimension definitions: `X=lon`,`L=lead`,`Y=lat`,`M=member`, `S=init`. We will rename the dimensions to their `climpred` names." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "fcstds=fcstds.rename({'S': 'init','L': 'lead','M': 'member', 'RMM1' : 'rmm1'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make sure that the `lead` dimension is set properly for `climpred`. SubX data stores `leads` as 0.5, 1.5, 2.5, etc, which correspond to 0, 1, 2, ... days since initialization. We will change the `lead` to be integers starting with zero. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "fcstds['lead']=(fcstds['lead']-0.5).astype('int')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to make sure that the `init` dimension is set properly for `climpred`. For daily data, the `init` dimension must be a `xr.CFTimeIndex` or a `pd.DatetimeIndex`. We convert the `init` values to `pd.DatetimeIndex`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "fcstds = decode_cf(fcstds, 'init')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`climpred` also requires that `lead` dimension has an attribute called `units` indicating what time units the `lead` is assocated with. Options are: `years,seasons,months,weeks,pentads,days`. For the SubX data, the `lead` `units` are `days`. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "fcstds['lead'].attrs={'units': 'days'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the `climpred HindcastEnsemble` object and add the observations." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "hindcast = HindcastEnsemble(fcstds)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "hindcast = hindcast.add_observations(obsds, 'obs')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the Anomaly Correlation Coefficient (ACC)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "skill = hindcast.verify(metric='acc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the skill as a function of lead time" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'ACC')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(skill['rmm1'])\n", "plt.title('GMAO-GEOS_V2p1 RMM1 Skill')\n", "plt.xlabel('Lead Time (Days)')\n", "plt.ylabel('ACC')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "\n", "1. Pegion, K., B.P. Kirtman, E. Becker, D.C. Collins, E. LaJoie, R. Burgman, R. Bell, T. DelSole, D. Min, Y. Zhu, W. Li, E. Sinsky, H. Guan, J. Gottschalck, E.J. Metzger, N.P. Barton, D. Achuthavarier, J. Marshak, R.D. Koster, H. Lin, N. Gagnon, M. Bell, M.K. Tippett, A.W. Robertson, S. Sun, S.G. Benjamin, B.W. Green, R. Bleck, and H. Kim, 2019: The Subseasonal Experiment (SubX): A Multimodel Subseasonal Prediction Experiment. Bull. Amer. Meteor. Soc., 100, 2043–2060, https://doi.org/10.1175/BAMS-D-18-0270.1\n", "\n", "2. Kirtman, B. P., Pegion, K., DelSole, T., Tippett, M., Robertson, A. W., Bell, M., … Green, B. W. (2017). The Subseasonal Experiment (SubX) [Data set]. IRI Data Library. https://doi.org/10.7916/D8PG249H\n", "\n", "3. Wheeler, M. C., & (null), H. H. (2004). An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Monthly Weather Review, 132(8), 1917–1932. http://doi.org/10.1175/1520-0493(2004)132\n", "\n", "4. Rashid, H. A., Hendon, H. H., Wheeler, M. C., & Alves, O. (2010). Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system. Climate Dynamics, 36(3-4), 649–661. http://doi.org/10.1007/s00382-010-0754-x\n", "\n", "5. Gottschalck, J., Wheeler, M., Weickmann, K., Vitart, F., Savage, N., Lin, H., et al. (2010). A Framework for Assessing Operational Madden–Julian Oscillation Forecasts: A CLIVAR MJO Working Group Project. Bulletin of the American Meteorological Society, 91(9), 1247–1258. http://doi.org/10.1175/2010BAMS2816.1" ] } ], "metadata": { "kernelspec": { "display_name": "Python (aoes)", "language": "python", "name": "aoes" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }