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446 lines
16 KiB
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================================
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Forecast Model Vertical Sounding
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================================
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`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Forecast_Model_Vertical_Sounding.ipynb>`_
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Python-AWIPS Tutorial Notebook
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--------------
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Objectives
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==========
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- Use python-awips to connect to an edex server
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- Request data using the `ModelSounding
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class <http://unidata.github.io/python-awips/api/ModelSounding.html>`__
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in addition to using the normal `DataAccess
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class <http://unidata.github.io/python-awips/api/DataAccessLayer.html>`__
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- Create and compare vertical sounding from different AWIPS model data
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with isobaric levels
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- Use `Shapely Point
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geometry <https://shapely.readthedocs.io/en/stable/reference/shapely.Point.html>`__
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to define a point
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- Convert between units when necessary
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- Use MetPy to create
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`SkewT <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html>`__
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and
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`Hodograph <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.Hodograph.html>`__
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plots
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--------------
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Table of Contents
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-----------------
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| `1
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Imports <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#imports>`__\
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| `2 EDEX
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Connection <http://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#edex-connection>`__\
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| `3 Define Useful
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Variables <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#define-useful-variables>`__\
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| `4 Function:
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get_surface_data() <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#function-get-surface-data>`__\
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| `5 Function:
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get_levels_data() <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#function-get-levels-data>`__\
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| `6 Function:
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plot_skewT() <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#function-plot-skewt>`__\
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| `7 Retrieve Necessary Plotting
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Data <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#retrieve-necessary-plotting-data>`__\
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| `8
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Skew-T/Log-P <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#skew-t-log-p>`__\
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| `9 Model Sounding
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Comparison <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#model-sounding-comparison>`__\
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| `10 See
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Also <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#see-also>`__\
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| `10.1 Related
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Notebooks <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#related-notebooks>`__\
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| `10.2 Additional
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Documentation <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html#additional-documentation>`__\
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1 Imports
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---------
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The imports below are used throughout the notebook. Note the first
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import is coming directly from python-awips and allows us to connect to
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an EDEX server. The subsequent imports are for data manipulation and
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visualization.
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.. code:: ipython3
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from awips.dataaccess import DataAccessLayer, ModelSounding
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import matplotlib.pyplot as plt
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import numpy as np
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from metpy.plots import SkewT, Hodograph
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from metpy.units import units
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes
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from math import sqrt
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from shapely.geometry import Point
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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2 EDEX Connection
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-----------------
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First we establish a connection to Unidata’s public EDEX server. This
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sets the proper server on the **DataAccessLayer**, which we will use
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numerous times throughout the notebook.
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.. code:: ipython3
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server = 'edex-beta.unidata.ucar.edu'
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DataAccessLayer.changeEDEXHost(server)
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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3 Define Useful Variables
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-------------------------
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The plotting in this notebook needs a model name, a location point
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(defined by latitude and longitude), and the most recent time range with
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the initial forecast run.
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.. code:: ipython3
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# Note the order is Lon,Lat and not Lat,Lon
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point = Point(-104.67,39.87)
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model="NAM40"
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# Get latest forecast cycle run
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timeReq = DataAccessLayer.newDataRequest("grid")
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timeReq.setLocationNames(model)
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cycles = DataAccessLayer.getAvailableTimes(timeReq, True)
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times = DataAccessLayer.getAvailableTimes(timeReq)
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fcstRun = DataAccessLayer.getForecastRun(cycles[-2], times)
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timeRange = [fcstRun[0]]
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print("Using " + model + " forecast time " + str(timeRange))
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.. parsed-literal::
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Using NAM40 forecast time [<DataTime instance: 2023-07-25 12:00:00 >]
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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4 Function: get_surface_data()
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------------------------------
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This function is used to get the initial forecast model data for surface
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height. This is done separately from the rest of the heights to
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determine the surface pressure. It uses the
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`ModelSounding <http://unidata.github.io/python-awips/api/ModelSounding.html>`__
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data object from python-awips to retrieve all the relevant information.
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This function takes the model name, location, and time as attributes,
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and returns arrays for pressure, temperature, dewpoint, and the u and v
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wind components.
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.. code:: ipython3
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def get_surface_data(modelName, location, time):
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""" model name, location, and timeRange desire """
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# request data and sort response
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pressure,temp,dpt,ucomp,vcomp = [],[],[],[],[]
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use_parms = ['T','DpT','uW','vW','P']
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use_level = "0.0FHAG"
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sndObject = ModelSounding.getSounding(modelName, use_parms, [use_level], location, time)
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if len(sndObject) > 0:
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for time in sndObject._dataDict:
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pressure.append(float(sndObject._dataDict[time][use_level]['P']))
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temp.append(float(sndObject._dataDict[time][use_level]['T']))
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dpt.append(float(sndObject._dataDict[time][use_level]['DpT']))
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ucomp.append(float(sndObject._dataDict[time][use_level]['uW']))
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vcomp.append(float(sndObject._dataDict[time][use_level]['vW']))
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print("Found surface record at " + "%.1f" % pressure[0] + "MB")
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else:
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raise ValueError("sndObject returned empty for query ["
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+ ', '.join(str(x) for x in (modelName, use_parms, point, use_level)) +"]")
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# return information for plotting
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return pressure,temp,dpt,ucomp,vcomp
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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5 Function: get_levels_data()
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-----------------------------
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This function is similar to *get_surface_data()*, except it gets data
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values for presure heights above the surface. It uses the
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`ModelSounding <http://unidata.github.io/python-awips/api/ModelSounding.html>`__
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data object from python-awips to retrieve all the relevant information.
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It takes the model name, location, and time (similar to the other
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function), and also takes the instantiated pressure, temperature,
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dewpoint, and wind vector arrays.
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It returns the fully populated pressure, temperature, dewpoint,
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u-component, v-component, and computed wind arrays.
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.. code:: ipython3
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def get_levels_data(modelName, location, time, pressure, temp, dpt, ucomp, vcomp):
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# Get isobaric levels with our requested parameters
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parms = ['T','DpT','uW','vW']
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levelReq = DataAccessLayer.newDataRequest("grid", envelope=point)
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levelReq.setLocationNames(model)
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levelReq.setParameters(*(parms))
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availableLevels = DataAccessLayer.getAvailableLevels(levelReq)
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# Clean levels list of unit string (MB, FHAG, etc.)
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levels = []
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for lvl in availableLevels:
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name=str(lvl)
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if 'MB' in name and '_' not in name:
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# If this level is above (less than in mb) our 0.0FHAG record
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if float(name.replace('MB','')) < pressure[0]:
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levels.append(lvl)
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# Get Sounding
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sndObject = ModelSounding.getSounding(modelName, parms, levels, location, time)
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if not len(sndObject) > 0:
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raise ValueError("sndObject returned empty for query ["
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+ ', '.join(str(x) for x in (model, parms, point, levels)) +"]")
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for time in sndObject._dataDict:
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for lvl in sndObject._dataDict[time].levels():
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for parm in sndObject._dataDict[time][lvl].parameters():
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if parm == "T":
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temp.append(float(sndObject._dataDict[time][lvl][parm]))
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elif parm == "DpT":
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dpt.append(float(sndObject._dataDict[time][lvl][parm]))
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elif parm == 'uW':
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ucomp.append(float(sndObject._dataDict[time][lvl][parm]))
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elif parm == 'vW':
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vcomp.append(float(sndObject._dataDict[time][lvl][parm]))
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else:
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print("WHAT IS THIS")
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print(sndObject._dataDict[time][lvl][parm])
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# Pressure is our requested level rather than a returned parameter
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pressure.append(float(lvl.replace('MB','')))
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# convert to numpy.array()
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pressure = np.array(pressure, dtype=float)
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temp = (np.array(temp, dtype=float) - 273.15) * units.degC
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dpt = (np.array(dpt, dtype=float) - 273.15) * units.degC
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ucomp = (np.array(ucomp, dtype=float) * units('m/s')).to('knots')
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vcomp = (np.array(vcomp, dtype=float) * units('m/s')).to('knots')
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wind = np.sqrt(ucomp**2 + vcomp**2)
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print("Using " + str(len(levels)) + " levels between " +
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str("%.1f" % max(pressure)) + " and " + str("%.1f" % min(pressure)) + "MB")
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return pressure,temp,dpt,ucomp,vcomp,wind
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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6 Function: plot_skewT()
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------------------------
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Since we’re plotting many different models for comparison, all that code
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was used to create this function.
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The function takes the model name, reference time, and the pressure,
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temperature, dewpoint, u-component, v-component, and wind arrays. It
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plots a skewT and hodograph using metpy.
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.. code:: ipython3
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def plot_skewT(modelName, pressure, temp, dpt, ucomp, vcomp, wind, refTime):
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plt.rcParams['figure.figsize'] = (12, 14)
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# Skew-T
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skew = SkewT(rotation=45)
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skew.plot(pressure, temp, 'r', linewidth=2)
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skew.plot(pressure, dpt, 'g', linewidth=2)
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skew.plot_barbs(pressure, ucomp, vcomp)
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skew.plot_dry_adiabats()
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skew.plot_moist_adiabats()
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skew.plot_mixing_lines(linestyle=':')
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skew.ax.set_ylim(1000, np.min(pressure))
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skew.ax.set_xlim(-50, 40)
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# Title
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plt.title(modelName + " (" + str(point) + ") " + str(refTime))
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# Hodograph
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ax_hod = inset_axes(skew.ax, '40%', '40%', loc=2)
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h = Hodograph(ax_hod, component_range=max(wind.magnitude))
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h.add_grid(increment=20)
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h.plot_colormapped(ucomp, vcomp, wind)
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# Dotted line at 0C isotherm
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l = skew.ax.axvline(0, color='c', linestyle='-', linewidth=1)
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plt.show()
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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7 Retrieve Necessary Plotting Data
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----------------------------------
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First we get the initial data at surface level using the
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get_surface_data function, and then pass those initial data arrays onto
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the get_levels_data request to finish populating for additional heights
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needed for Skew-T plots. We want to keep track of the pressure,
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temeperature, dewpoint, u-component, v-component, and wind arrays so we
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store them in variables to use later on.
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.. code:: ipython3
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p,t,d,u,v = get_surface_data(model,point,timeRange)
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p,t,d,u,v,w = get_levels_data(model,point,timeRange,p,t,d,u,v)
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.. parsed-literal::
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Found surface record at 833.2MB
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Using 32 levels between 833.2 and 50.0MB
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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8 Skew-T/Log-P
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--------------
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Here we use our plot_skewT function to generate our skewT & hodograph
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charts for the data we retreived so far. This is where the pressure,
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temperature, dewpoint, and wind data is needed.
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.. code:: ipython3
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plot_skewT(model, p, t, d, u, v, w, timeRange[0])
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.. image:: Forecast_Model_Vertical_Sounding_files/Forecast_Model_Vertical_Sounding_25_0.png
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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9 Model Sounding Comparison
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---------------------------
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Now that we know how to retreive and plot the data for one model, we can
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run a loop to retreive data for various models and plot them for
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comparison. In this example we’ll also plot RAP13 and GFS20 data to
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compare with NAM40.
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This is also where our functions become so important, because we can
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easily recall all that logic and keep this for-loop fairly simple.
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.. code:: ipython3
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models = ["RAP13", "GFS20", "NAM40"]
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for modelName in models:
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timeReq = DataAccessLayer.newDataRequest("grid")
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timeReq.setLocationNames(modelName)
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cycles = DataAccessLayer.getAvailableTimes(timeReq, True)
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times = DataAccessLayer.getAvailableTimes(timeReq)
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fr = DataAccessLayer.getForecastRun(cycles[-1], times)
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print("Using " + modelName + " forecast time " + str(fr[0]))
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tr = [fr[0]]
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p,t,d,u,v = get_surface_data(modelName,point,tr)
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p,t,d,u,v,w = get_levels_data(modelName,point,tr,p,t,d,u,v)
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# Skew-T
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plot_skewT(modelName,p,t,d,u,v,w,tr[0])
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.. parsed-literal::
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Using RAP13 forecast time 2023-07-25 19:00:00
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Found surface record at 839.4MB
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Using 32 levels between 839.4 and 100.0MB
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.. image:: Forecast_Model_Vertical_Sounding_files/Forecast_Model_Vertical_Sounding_28_1.png
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.. parsed-literal::
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Using GFS20 forecast time 2023-07-25 12:00:00
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Found surface record at 842.5MB
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Using 32 levels between 842.5 and 100.0MB
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.. image:: Forecast_Model_Vertical_Sounding_files/Forecast_Model_Vertical_Sounding_28_3.png
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.. parsed-literal::
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Using NAM40 forecast time 2023-07-25 18:00:00
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Found surface record at 833.8MB
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Using 32 levels between 833.8 and 50.0MB
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.. image:: Forecast_Model_Vertical_Sounding_files/Forecast_Model_Vertical_Sounding_28_5.png
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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10 See Also
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-----------
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10.1 Related Notebooks
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~~~~~~~~~~~~~~~~~~~~~~
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- `Grid Levels and
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Parameters <https://unidata.github.io/python-awips/examples/generated/Grid_Levels_and_Parameters.html>`__
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- `Upper Air BUFR
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Soundings <http://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html>`__
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- `Model Sounding
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Data <http://unidata.github.io/python-awips/examples/generated/Model_Sounding_Data.html>`__
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10.2 Additional Documentation
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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**python-awips:**
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- `awips.DataAccessLayer <http://unidata.github.io/python-awips/api/DataAccessLayer.html>`__
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- `awips.ModelSounding <https://unidata.github.io/python-awips/api/ModelSounding.html>`__
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**matplotlib:**
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- `matplotlib.pyplot <https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.html>`__
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**MetPy**
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- `metpy.wind_speed <https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.wind_speed.html>`__
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- `metpy.wind_direction <https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.wind_direction.html>`__
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- `metpy.vapor_pressure <https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.vapor_pressure.html>`__
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- `metpy.dewpoint <https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.dewpoint.html>`__
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- `metpy.skewt <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html>`__
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- `metpy.hodograph <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.Hodograph.html>`__
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`Top <https://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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--------------
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