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Updated documentation, descriptions for v1.0 release.
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NARR data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web
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site at https://psl.noaa.gov/
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The original "air.201806.nc" datafile was downloaded from
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https://psl.noaa.gov/data/gridded/data.narr.pressure.html
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Specific download link (~650 MB):
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ftp://ftp.cdc.noaa.gov/Datasets/NARR/pressure/air.201806.nc [Last accessed on
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2020-06-17]
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The following files are a direct slice of this data:
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- export_dist.txt
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- export_heights.txt
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- export_press_atmosphere.txt
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- export_temp_atmosphere.txt
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This data file was not included here, because it has a very niche use for the
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AstroAtmosphere package and is a large file. The tradeoff seems to me to not very
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favourable, and therefore it is omitted.
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@@ -0,0 +1,97 @@
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NCEP North American Regional Reanalysis (NARR) gridded weather data.
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Data from https://www.esrl.noaa.gov/psd/data/gridded/data.narr.pressure.html
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###############
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# Header info #
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###############
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$ ncdump -h air.201806.nc
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netcdf air.201806 {
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dimensions:
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time = UNLIMITED ; // (240 currently)
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level = 29 ;
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y = 277 ;
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x = 349 ;
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variables:
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double time(time) ;
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time:axis = "T" ;
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time:coordinate_defines = "point" ;
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time:delta_t = "0000-00-00 03:00:00" ;
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time:long_name = "Time" ;
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time:standard_name = "time" ;
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time:units = "hours since 1800-1-1 00:00:0.0" ;
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time:actual_range = 1914576., 1915293. ;
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float level(level) ;
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level:GRIB_id = 100s ;
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level:GRIB_name = "hPa" ;
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level:actual_range = 1000.f, 100.f ;
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level:axis = "Z" ;
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level:coordinate_defines = "point" ;
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level:long_name = "Level" ;
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level:positive = "down" ;
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level:standard_name = "level" ;
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level:units = "millibar" ;
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float lat(y, x) ;
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lat:axis = "Y" ;
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lat:coordinate_defines = "point" ;
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lat:long_name = "Latitude" ;
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lat:standard_name = "latitude" ;
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lat:units = "degrees_north" ;
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float lon(y, x) ;
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lon:axis = "X" ;
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lon:coordinate_defines = "point" ;
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lon:long_name = "Longitude" ;
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lon:standard_name = "longitude" ;
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lon:units = "degrees_east" ;
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float y(y) ;
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y:long_name = "northward distance from southwest corner of domain in projection coordinates" ;
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y:standard_name = "projection_y_coordinate" ;
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y:units = "m" ;
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float x(x) ;
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x:long_name = "eastward distance from southwest corner of domain in projection coordinates" ;
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x:standard_name = "projection_x_coordinate" ;
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x:units = "m" ;
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int Lambert_Conformal ;
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Lambert_Conformal:false_easting = 5632642.22547 ;
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Lambert_Conformal:false_northing = 4612545.65137 ;
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Lambert_Conformal:grid_mapping_name = "lambert_conformal_conic" ;
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Lambert_Conformal:latitude_of_projection_origin = 50. ;
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Lambert_Conformal:longitude_of_central_meridian = -107. ;
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Lambert_Conformal:standard_parallel = 50., 50. ;
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float air(time, level, y, x) ;
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air:GRIB_id = 11s ;
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air:GRIB_name = "TMP" ;
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air:_FillValue = 9.96921e+36f ;
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air:coordinates = "lat lon" ;
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air:dataset = "NARR 3-hourly" ;
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air:grid_mapping = "Lambert_Conformal" ;
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air:level_desc = "Pressure Levels" ;
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air:long_name = "3-hourly Air Temperature on Pressure Levels" ;
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air:parent_stat = "Other" ;
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air:standard_name = "air_temperature" ;
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air:statistic = "Individual Obs" ;
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air:units = "degK" ;
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air:valid_range = 137.5f, 362.5f ;
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air:var_desc = "Air temperature" ;
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air:missing_value = -9.96921e+36f ;
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air:actual_range = 187.1279f, 320.1487f ;
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// global attributes:
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:Conventions = "CF-1.2" ;
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:centerlat = 50. ;
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:centerlon = -107. ;
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:comments = "" ;
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:institution = "National Centers for Environmental Prediction" ;
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:latcorners = 1.000001f, 0.897945f, 46.3544f, 46.63433f ;
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:loncorners = -145.5f, -68.32005f, -2.569891f, 148.6418f ;
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:platform = "Model" ;
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:standardpar1 = 50. ;
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:standardpar2 = 50.000001 ;
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:title = "8x Daily NARR" ;
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:history = "created Mon Jul 18 21:01:07 MDT 2016 by NOAA/ESRL/PSD" ;
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:dataset_title = "NCEP North American Regional Reanalysis (NARR)" ;
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:references = "https://www.esrl.noaa.gov/psd/data/gridded/data.narr.html" ;
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:source = "http://www.emc.ncep.noaa.gov/mmb/rreanl/index.html" ;
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}
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@@ -0,0 +1 @@
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100000.00000000001, 100000.00000000001, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.00000000001, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100Line truncated
|
||||
@@ -0,0 +1 @@
|
||||
[[284.050537109375, 284.05081584207153, 284.0510945747681, 284.0513733074647, 284.0516520401612, 284.0519307728577, 284.0522095055543, 284.05248823825076, 284.05276697094735, 284.05304570364393, 284.05332443634046, 284.053603169037, 284.0538819017336, 284.05416063443016, 284.0544393671267, 284.05471809982316, 284.05499683251975, 284.0552755652163, 284.0555542979128, 284.05583303060934, 284.0561117633059, 284.05639049600245, 284.05666922869904, 284.0569479613956, 284.05722669409215, 284.0575054267887, 284.0577841594852, 284.05806289218174, 284.05834162487827, 284.0586203575748, 284.0588990902713, 284.05917782296797, 284.0594565556645, 284.059735288361, 284.06001402105755, 284.06029275375414, 284.06057148645067, 284.06085021914726, 284.0611289518438, 284.0614076845403, 284.06168641723684, 284.06196514993337, 284.06224388262996, 284.0625226153265, 284.062801348023, 284.0630800807196, 284.06335881341613, 284.0636375461127, 284.06391627880924, 284.0641950115058, 284.06447374420236, 284.06475247689883, 284.0650312095954, 284.06530994229195, 284.06558867498853, 284.06586740768506, 284.0661461403816, 284.0664248730782, 284.0667036057747, 284.06698233847123, 284.0672610711678, 284.06753980386435, 284.0678185365609, 284.0680972692574, 284.06837600195394, 284.0686547346505, 284.06893346734705, 284.06921220004364, 284.06949093274017, 284.0697696654367, 284.0700483981333, 284.0703271308298, 284.0706058635264, 284.07088459622287, 284.0711633289194, 284.071442061616, 284.0717207943125, 284.0719995270091, 284.0722782597056, 284.07255699240216, 284.0728357250987, 284.07311445779527, 284.07339319049186, 284.0736719231884, 284.0739506558849, 284.07422938858144, 284.07450812127803, 284.0747868539745, 284.0750655866711, 284.0753443193677, 284.0756230520642, 284.07590178476073, 284.07618051745726, 284.0764592501538, 284.0767379828504, 284.0770167155469, 284.0772954482435, 284.07757418094, 284.07785291363655, 284.0781316463331, 284.07841037902966, 284.0786891117262, 284.0789678444228, 284.0792465771193, 284.07952530981584, 284.07980404251236, 284.08008277520895, 284.0803615079055, 284.080640240602, 284.0809189732986, 284.0811977059951, 284.08147643869165, 284.0817551713882, 284.08203390408477, 284.0823126367813, 284.0825913694779, 284.08287010217435, 284.08314883487094, 284.08342756756747, 284.08370630026405, 284.0839850329606, 284.0842637656571, 284.0845424983537, 284.0848212310502, 284.08509996374676, 284.08537869644334, 284.0856574291398, 284.0859361618364, 284.08621489453293, 284.0864936272295, 284.08677235992604, 284.0870510926226, 284.08732982531916, 284.0876085580157, 284.0878872907122, 284.0881660234088, 284.08844475610533, 284.08872348880186, 284.0890022214984, 284.089280954195, 284.0895596868915, 284.08983841958803, 284.0901171522846, 284.09039588498115, 284.0906746176777, 284.09095335037426, 284.0912320830708, 284.0915108157673, 284.09178954846385, 284.09206828116044, 284.09234701385697, 284.0926257465535, 284.0929044792501, 284.0931832119466, 284.09346194464314, 284.0937406773397, 284.09401941003625, 284.09429814273284, 284.0945768754293, 284.0948556081259, 284.0951343408224, 284.095413073519, 284.09569180621554, 284.09597053891207, 284.0962492716086, 284.0965280043052, 284.0968067370017, 284.0970854696983, 284.09736420239483, 284.106526646489, 284.1175328703515, 284.1285390942141, 284.13954531807667, 284.15055154193925, 284.1615577658018, 284.17256398966435, 284.1835702135269, 284.19457643738946, 284.205582661252, 284.21658888511456, 284.22759510897714, 284.23860133283966, 284.24960755670224, 284.2606137805648, 284.2716200044274, 284.2826262282899, 284.2936324521525, 284.3046386760151, 284.31564489987767, 284.3266511237402, 284.33765734760277, 284.3486635714653, 284.35966979532793, 284.37067601919046, 284.38168224305304, 284.39268846691556, 284.40369469077814, 284.4147009146407, 284.4257071385033, 284.4367133623658, 284.4477195862284, 284.458725810091, 284.4697320339535, 284.4807382578161, 284.49174448167867, 284.5027507055412, 284.51375692940377, 284.52476315326635, 284.53576937712893, 284.54677560099145, 284.55778182485403, 284.5687880487166, 284.5797942725792, 284.5908004964417, 284.6018067203043, 284.6128129441669, 284.6238191680294, 284.634825391892, 284.64583161575456, 284.6568378396171, 284.66784406347966, 284.67885028734224, 284.68985651120477, 284.70086273506735, 284.7118689589299, 284.7228751827925, 284.73388140665503, 284.7448876305176, 284.7558938543802, 284.76690007824277, 284.7779063021053, 284.7889125259679, 284.7999187498304, 284.810924973693, 284.82193119755556, 284.8329374214181, 284.84394364528066, 284.8549498691432, 284.86595609300576, 284.87696231686834, 284.8879685407309, 284.89897476459345, 284.909980988456, 284.9209872123186, 284.9319934361812, 284.9429996600437, 284.9540058839063, 284.96501210776887, 284.9760183316314, 284.987024555494, 284.99803077935655, 285.0090370032191, 285.0200432270817, 285.03104945094424, 285.04205567480676, 285.05306189866934, 285.0640681225319, 285.07507434639444, Line truncated
|
||||
@@ -0,0 +1,168 @@
|
||||
#!/usr/bin/env Python3
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.lines import Line2D
|
||||
from matplotlib import cm
|
||||
from matplotlib.colors import LogNorm
|
||||
from AstroAtmosphere import *
|
||||
from scipy import interpolate
|
||||
import json
|
||||
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
|
||||
@ Contact: born@astron.nl
|
||||
@ Description: This script uses the NCEP NARR data to model a real structured atmosphere
|
||||
from data with a resolution of 2.5 km per data point. We interpolate this
|
||||
data to a higher resolution.
|
||||
Then a full integration over this atmosphere is done and compared to the
|
||||
values for the refraction and dispersion if we had only taken the atmospheric
|
||||
conditions directly at the observatory.
|
||||
'''
|
||||
|
||||
|
||||
# Load a variable from a json text file.
|
||||
def loadVariableFromTXT(filename):
|
||||
variableFile = open(filename, 'r')
|
||||
variable = json.load(variableFile)
|
||||
variableFile.close()
|
||||
return np.asarray(variable)
|
||||
|
||||
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
|
||||
# h, _, _, _, Temp, Press, Dens, vSound, _,_ = np.genfromtxt('si1py_modified.txt', unpack=True, skip_header=6)
|
||||
|
||||
dist = loadVariableFromTXT('export_dist.txt')
|
||||
heights = loadVariableFromTXT('export_heights.txt')
|
||||
press = loadVariableFromTXT('export_press_atmosphere.txt')
|
||||
temp = loadVariableFromTXT('export_temp_atmosphere.txt')
|
||||
print("Loading complete!")
|
||||
|
||||
# Conditions at Cerro Armazones
|
||||
T = temp[0,0] # k
|
||||
P = press[0,0]# Pa
|
||||
H = 0.0
|
||||
xc = 314 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h0 = 0 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
zenith = 60 # degrees
|
||||
|
||||
# Calculate the index of refraction
|
||||
at = Observatory()
|
||||
n1 = at.n_tph(l=l1, T=T, p=P, RH=H, xc=xc)
|
||||
n2 = at.n_tph(l=l2, T=T, p=P, RH=H, xc=xc)
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
|
||||
RHlist = np.asarray([H]*len(press[:,0]))
|
||||
xclist = np.asarray([xc]*len(press[:,0]))
|
||||
Dens = at.rho(p=press[:,0], T=temp[:,0], RH=RHlist, xc=xclist)
|
||||
|
||||
refr = refraction(lat, h0)
|
||||
refr.setReducedHeight(P, rho)
|
||||
disp = dispersion(lat, h0)
|
||||
disp.setReducedHeight(P, rho)
|
||||
|
||||
|
||||
# We must create a cube of weather data:
|
||||
# We assume a flat atmosphere for now, so that h does not need to be adjusted
|
||||
# x_max = 200 km, gives zenith_max = ~66.7 degrees
|
||||
# Every 20 km in horizontal direction a new perturbation and interpolation
|
||||
# for every 500 m.
|
||||
x_max = dist[-1] # km
|
||||
|
||||
Temp_cube_n = []
|
||||
Press_cube_n = []
|
||||
|
||||
for x in range(len(dist)):
|
||||
Temp_cube_n.append(temp[:,0])
|
||||
Press_cube_n.append(press[:,0])
|
||||
|
||||
Temp_cube_n = np.asarray(Temp_cube_n).T
|
||||
Press_cube_n = np.asarray(Press_cube_n).T
|
||||
|
||||
|
||||
|
||||
fig = plt.figure()
|
||||
ax1 = fig.add_subplot(211)
|
||||
im = ax1.pcolormesh(dist,heights, temp, cmap=cm.coolwarm)
|
||||
fig.colorbar(im, ax=ax1, label='Temperature (K)')
|
||||
ax1.set_aspect(1)
|
||||
ax1.set_ylabel('Height (km)')
|
||||
|
||||
ax2 = fig.add_subplot(212)
|
||||
im2 = ax2.pcolormesh(dist, heights, press, cmap=cm.coolwarm, norm=LogNorm())
|
||||
ax2.set_aspect(1)
|
||||
ax2.set_xlabel('Horizontal distance (km)')
|
||||
ax2.set_ylabel('Height (km)')
|
||||
fig.colorbar(im2, ax=ax2, label='Pressure (Pa)')
|
||||
plt.tight_layout()
|
||||
plt.show(block=False)
|
||||
|
||||
# Now for a given zenith angle we need to find the temp and press along the line of sight
|
||||
f_Temp_2d = interpolate.interp2d(dist, heights, temp, kind='linear')
|
||||
f_Press_2d = interpolate.interp2d(dist, heights, press, kind='linear')
|
||||
f_Temp_2d_n = interpolate.interp2d(dist, heights, Temp_cube_n, kind='linear')
|
||||
f_Press_2d_n = interpolate.interp2d(dist, heights, Press_cube_n, kind='linear')
|
||||
print('Interpolation complete!')
|
||||
|
||||
|
||||
|
||||
zenithRange = np.linspace(30,65,10)
|
||||
refrDiff, dispDiff = [], []
|
||||
|
||||
for zenith in zenithRange:
|
||||
z = np.deg2rad(zenith)
|
||||
y_los = heights
|
||||
x_los = np.linspace(0, heights.max()*np.tan(z), len(heights))
|
||||
|
||||
Temp_los, Press_los = [], []
|
||||
Temp_los_n, Press_los_n = [], []
|
||||
for i in range(len(heights)):
|
||||
Temp_los.append(f_Temp_2d(x_los[i], y_los[i])[0])
|
||||
Press_los.append(f_Press_2d(x_los[i], y_los[i])[0])
|
||||
Temp_los_n.append(f_Temp_2d_n(x_los[i], y_los[i])[0])
|
||||
Press_los_n.append(f_Press_2d_n(x_los[i], y_los[i])[0])
|
||||
|
||||
Temp_los = np.asarray(Temp_los)
|
||||
Press_los = np.asarray(Press_los)
|
||||
Temp_los_n = np.asarray(Temp_los_n)
|
||||
Press_los_n = np.asarray(Press_los_n)
|
||||
rho_los = at.rho(p=Press_los, T=Temp_los, RH=H, xc=xc)
|
||||
rho_los_n = at.rho(p=Press_los_n, T=Temp_los_n, RH=H, xc=xc)
|
||||
|
||||
refrInt = refr.refractionIntegral(n1, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los_n)*3600*1000
|
||||
refrInt_pert = refr.refractionIntegral(n1, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los)*3600*1000
|
||||
refrDiff.append(refrInt_pert - refrInt)
|
||||
|
||||
dispInt = disp.refractionIntegral(n1, n2, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los_n)*3.6e6
|
||||
dispInt_pert = disp.refractionIntegral(n1, n2, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los)*3.6e6
|
||||
dispDiff.append(dispInt_pert - dispInt)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
fig3 = plt.figure(figsize=(9,7))
|
||||
ax1 = fig3.add_subplot(211)
|
||||
ax2 = fig3.add_subplot(212)
|
||||
ax1.tick_params(axis ='both', direction='in', which='both', labelsize=16)
|
||||
ax2.tick_params(axis ='both', direction='in', which='both', labelsize=16)
|
||||
ax1.grid(True, alpha=0.3)#, which='major')
|
||||
ax2.grid(True, alpha=0.3)#, which='major')
|
||||
|
||||
|
||||
ax1.plot(zenithRange, np.log10(np.abs(refrDiff)), 'k-', linewidth=2)
|
||||
ax2.plot(zenithRange, np.log10(np.abs(dispDiff)), 'k-', linewidth=2)
|
||||
ax1.set_xlabel('Zenith (degrees)', fontsize=18)
|
||||
ax2.set_xlabel('Zenith (degrees)', fontsize=18)
|
||||
ax1.set_ylabel('$\log_{10}($|$R - R_{o}$|$)$ (mas)', fontsize=18)
|
||||
ax2.set_ylabel('$\log_{10}($|$\Delta R - \Delta R_{o}$|$)$ (mas)', fontsize=18)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,251 @@
|
||||
#!/usr/bin/env Python3
|
||||
import numpy as np
|
||||
import sys
|
||||
from AstroAtmosphere import *
|
||||
import matplotlib.pyplot as plt
|
||||
from netCDF4 import Dataset
|
||||
from pdb import set_trace
|
||||
from matplotlib import cm
|
||||
from scipy.interpolate import interp1d, interp2d
|
||||
from matplotlib.colors import LogNorm
|
||||
import json
|
||||
|
||||
# Saves a variable, preferably a list or string to json text file.
|
||||
# Useful for long simulations, where you're making a plot
|
||||
def saveVariableToTXT(variable, filename):
|
||||
variableFile = open(filename, 'w')
|
||||
|
||||
if type(variable) == np.ndarray:
|
||||
json.dump(variable.tolist(), variableFile)
|
||||
elif type(variable) in [list, str, float]:
|
||||
json.dump(variable, variableFile)
|
||||
else:
|
||||
print('Not a list, array or string. Check variable type.')
|
||||
|
||||
variableFile.close()
|
||||
return
|
||||
|
||||
# Load a variable from a json text file.
|
||||
def loadVariableFromTXT(filename):
|
||||
variableFile = open(filename, 'r')
|
||||
variable = json.load(variableFile)
|
||||
variableFile.close()
|
||||
return variable
|
||||
|
||||
|
||||
def calculateDistanceResolution(lat, lon):
|
||||
# Calculate local radius of the earth
|
||||
_A = 6378.137 # km
|
||||
_B = 6356.752 # km
|
||||
_phi = np.deg2rad(lat)
|
||||
_rc0 = (_A*_B)**2 / (_A**2 * np.cos(_phi)**2 + _B**2 * np.sin(_phi)**2)**(3/2)
|
||||
|
||||
# Calculate circumference of the earth at constant latitude.
|
||||
latR = 2 * np.pi * np.cos(_phi) * _rc0
|
||||
distRes = latR/360 * lon
|
||||
return distRes
|
||||
|
||||
def H_isotherm(T, lat):
|
||||
'''
|
||||
Calculates the reduced height of the atmosphere, assuming an isotherm distribution.
|
||||
'''
|
||||
_kb = 1.38064852e-23 # kg m2 s-2 K-1
|
||||
_m = 4.809651698e-26 # kg (weight per molecule of air, assuming dry air)
|
||||
|
||||
_phi = np.deg2rad(lat)
|
||||
_c1 = 5.2790414e-3
|
||||
_c2 = 2.32718e-5
|
||||
_c3 = 1.262e-7
|
||||
_c4 = 7e-10
|
||||
_g0 = 9.780327 # m/s^2
|
||||
_g0_local = _g0 * (1 + _c1 * np.sin(_phi)**2 + _c2 * np.sin(_phi)**4 + _c3 * np.sin(_phi)**6 + _c4 * np.sin(_phi)**8)
|
||||
return (_kb*T) / (_m * _g0_local)
|
||||
|
||||
|
||||
def calculatePressure(Th, P0, h, lat):
|
||||
redH = H_isotherm(Th, lat)
|
||||
Ph = P0 * np.exp(-h/redH)
|
||||
return Ph
|
||||
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
|
||||
# Import the real data
|
||||
nc = Dataset('air.201806.nc', 'r')
|
||||
latitudes = np.asarray(nc.variables['lat'][:])
|
||||
longitudes = np.asarray(nc.variables['lon'][:])
|
||||
air_temp = nc.variables['air']
|
||||
air_press = np.asarray(nc.variables['level'])
|
||||
|
||||
|
||||
|
||||
|
||||
at = Observatory()
|
||||
|
||||
# Parameters at Cerro Armazones
|
||||
T = air_temp[0,:,0,0][0] # k
|
||||
P = air_press[0] # Pa
|
||||
H = 0
|
||||
xc = 450 # ppm
|
||||
lat = 24.44768
|
||||
h = 0 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
disp = dispersion(lat, h)
|
||||
disp.setReducedHeight(P, rho)
|
||||
|
||||
air_dens = at.rho(p=air_press, T=air_temp[0,:,0,0], RH=np.asarray([0]*len(air_press)), xc=np.asarray([0]*len(air_press)))
|
||||
|
||||
heights = disp.calcHeight(air_press, air_temp[0,:,0,0], RH=np.asarray([0]*len(air_press)), xc=np.asarray([0]*len(air_press)), P0=air_press[0]) / 1e3
|
||||
distance_lat = calculateDistanceResolution(latitudes[100], longitudes[100,:]) - calculateDistanceResolution(latitudes[100,0], longitudes[100,0])
|
||||
temp_selection_ground = air_temp[0,0,100,:]
|
||||
temp_selection_upper = air_temp[0,-1,100,:]
|
||||
print(distance_lat[1])
|
||||
|
||||
|
||||
#########################################################
|
||||
# Extrapolating using the U.S. Standard Atmosphere 1976.
|
||||
# The end point of the real data is used to shift the USSA data accordingly (linearly)
|
||||
h_ussa1976, _, _, _, air_temp_ussa1976, air_press_ussa1976,_,_,_,_ = np.genfromtxt('../US1976 Standard Atmosphere/si1py_modified.txt', unpack=True, skip_header=6)
|
||||
temp_ip = interp1d(h_ussa1976, air_temp_ussa1976)
|
||||
press_ip = interp1d(h_ussa1976, air_press_ussa1976)
|
||||
|
||||
ussa1976_start = temp_ip(heights[-1])
|
||||
diff_temp_list = temp_selection_upper - ussa1976_start
|
||||
start_i = np.argmax(h_ussa1976 > heights[-1])
|
||||
ussa1976_temp_array = np.tile(air_temp_ussa1976[start_i:], (len(temp_selection_upper),1))
|
||||
ussa1976_temp_array = np.asarray([ussa1976_temp_array[i] + diff_temp_list[i] for i in range(len(diff_temp_list))]).T
|
||||
|
||||
# Combining the (masked) arrays
|
||||
new_heights = np.append(heights, h_ussa1976[start_i:])
|
||||
tempData = np.append(air_temp[0,:,100,:], ussa1976_temp_array, axis=0)
|
||||
tempData_mask = np.append(air_temp[0,:,100,:].mask, np.zeros(ussa1976_temp_array.shape), axis=0)
|
||||
tempData = np.ma.masked_array(tempData, mask=tempData_mask)
|
||||
|
||||
# Now the same for the pressure data
|
||||
# Combining the (masked) arrays
|
||||
start_i = np.argmax(h_ussa1976 > heights[-1])
|
||||
new_heights = np.append(heights, h_ussa1976[start_i:])
|
||||
pressData = []
|
||||
for i in range(len(temp_selection_upper)):
|
||||
calc_press_data = calculatePressure(Th=ussa1976_temp_array[:,i], P0=air_press[0], h=h_ussa1976[start_i:]*1000, lat=lat)
|
||||
pressData_i = np.append(air_press*100, calc_press_data*100, axis=0)
|
||||
pressData.append(pressData_i)
|
||||
pressData = np.asarray(pressData).T
|
||||
#########################################################
|
||||
|
||||
# We'd now like to export a subset of the atmospheric data for a distance of ~200 km.
|
||||
# Let's use interpolation to create a uniform gridded dataset
|
||||
export_heights = np.linspace(0,new_heights.max(),400)
|
||||
export_dist = np.linspace(0,distance_lat[136]-distance_lat[130],1000)
|
||||
atmosphere_temp_func = interp2d(distance_lat[130:136]-distance_lat[130], new_heights, tempData[:,130:136], kind='linear')
|
||||
export_temp_atmosphere = atmosphere_temp_func(export_dist, export_heights)
|
||||
|
||||
atmosphere_press_func = interp2d(distance_lat[130:136]-distance_lat[130], new_heights, pressData[:,130:136], kind='linear')
|
||||
export_press_atmosphere = atmosphere_press_func(export_dist, export_heights)
|
||||
|
||||
# saveVariableToTXT(export_heights, 'export_heights.txt')
|
||||
# saveVariableToTXT(export_dist, 'export_dist.txt')
|
||||
# saveVariableToTXT(export_temp_atmosphere, 'export_temp_atmosphere.txt')
|
||||
# saveVariableToTXT(export_press_atmosphere, 'export_press_atmosphere.txt')
|
||||
|
||||
|
||||
# Plot the real data + extrapolated standard atmosphere.
|
||||
# fig = plt.figure()
|
||||
# ax = fig.add_subplot(211)
|
||||
# ax2 = fig.add_subplot(212)
|
||||
# im1 = ax.pcolormesh(export_dist, export_heights, export_temp_atmosphere, cmap=cm.coolwarm)
|
||||
# im2 = ax2.pcolormesh(export_dist, export_heights, export_press_atmosphere, cmap=cm.coolwarm, norm=LogNorm())
|
||||
# ax2.set_xlabel('Lateral distance (km)')
|
||||
# ax.set_ylabel('Height (km)')
|
||||
# ax2.set_ylabel('Height (km)')
|
||||
# cbar = fig.colorbar(im1, ax=ax, label='Temperature (K)')
|
||||
# lbls = cbar.get_ticks()
|
||||
# cbar.ax.invert_yaxis()
|
||||
# cbar.set_ticks(lbls[::-1])
|
||||
# cbar2 = fig.colorbar(im2, ax=ax2, label='Pressure (Pa)')
|
||||
# lbls = cbar2.get_ticks()
|
||||
# cbar2.ax.invert_yaxis()
|
||||
# cbar2.set_ticks(lbls[::-1])
|
||||
# plt.tight_layout()
|
||||
# plt.show(block=False)
|
||||
# set_trace()
|
||||
|
||||
|
||||
|
||||
|
||||
# # Plot the real data + extrapolated standard atmosphere.
|
||||
# fig = plt.figure()
|
||||
# ax = fig.add_subplot(211)
|
||||
# ax2 = fig.add_subplot(212)
|
||||
# im1 = ax.imshow(tempData[:,130:137], cmap=cm.coolwarm, interpolation= 'bicubic', aspect='auto', origin='lower')
|
||||
# # im1 = ax.pcolormesh(distance_lat[130:137], new_heights, tempData[:,130:137], cmap=cm.coolwarm)
|
||||
# im2 = ax2.pcolormesh(distance_lat, new_heights, pressData, cmap=cm.coolwarm, norm=LogNorm())
|
||||
# ax2.set_xlabel('Lateral distance (km)')
|
||||
# ax.set_ylabel('Height (km)')
|
||||
# ax2.set_ylabel('Height (km)')
|
||||
# cbar = fig.colorbar(im1, ax=ax, label='Temperature (K)')
|
||||
# lbls = cbar.get_ticks()
|
||||
# cbar.ax.invert_yaxis()
|
||||
# cbar.set_ticks(lbls[::-1])
|
||||
# cbar2 = fig.colorbar(im2, ax=ax2, label='Pressure (Pa)')
|
||||
# lbls = cbar2.get_ticks()
|
||||
# cbar2.ax.invert_yaxis()
|
||||
# cbar2.set_ticks(lbls[::-1])
|
||||
# plt.tight_layout()
|
||||
# plt.show(block=False)
|
||||
|
||||
|
||||
# Plot the real data and properties
|
||||
fig = plt.figure(figsize=(9,4))
|
||||
fig2 = plt.figure()
|
||||
ax1 = fig2.add_subplot(221)
|
||||
ax2 = fig2.add_subplot(222)
|
||||
ax3 = fig2.add_subplot(223)
|
||||
ax4 = fig.add_subplot(121)
|
||||
ax5 = fig2.add_subplot(224)
|
||||
ax6 = fig.add_subplot(122)
|
||||
|
||||
ax1.grid(alpha=0.3)
|
||||
ax2.grid(alpha=0.3)
|
||||
ax3.grid(alpha=0.3)
|
||||
ax5.grid(alpha=0.3)
|
||||
|
||||
ax1.plot(air_temp[0,:,0,0], heights)
|
||||
ax1.set_xlabel('Temperature (K)')
|
||||
ax1.set_ylabel('Height (km)')
|
||||
|
||||
ax2.plot(air_press, heights)
|
||||
ax2.set_xlabel('Pressure (Pa)')
|
||||
ax2.set_ylabel('Height (km)')
|
||||
|
||||
ax3.plot(air_dens, heights)
|
||||
ax3.set_xlabel('Density (g cm$^{-3}$)')
|
||||
ax3.set_ylabel('Height (km)')
|
||||
|
||||
im = ax4.imshow(np.rot90(air_temp[0,0,:,:].T), aspect=1, cmap='jet')
|
||||
ax4.set_yticks([])
|
||||
ax4.set_xticks([])
|
||||
# fig.colorbar(im, ax=ax4, label='Temperature (K)')
|
||||
|
||||
ax5.plot(distance_lat, temp_selection_ground, 'b-', label='ground')
|
||||
ax5.plot(distance_lat, temp_selection_upper, 'r-', label='upper atmosphere')
|
||||
ax5.legend()
|
||||
ax5.set_ylabel('Temperature (K)')
|
||||
ax5.set_xlabel('Distance (km)')
|
||||
|
||||
im2 = ax6.imshow(air_temp[0,:,100,:], aspect='auto', origin='lower', interpolation='bilinear', extent=[0,distance_lat[-1],0,heights[-1] ], cmap='jet')
|
||||
ax6.set_xlabel('Distance (km)', fontsize=18)
|
||||
ax6.set_ylabel('Height (km)', fontsize=18)
|
||||
cbar = fig.colorbar(im2, ax=ax6)#, label='Temperature (K)')
|
||||
cbar.ax.tick_params(labelsize=16)
|
||||
cbar.set_label('Temperature (K)', size=18)
|
||||
lbls = cbar.get_ticks()
|
||||
cbar.ax.invert_yaxis()
|
||||
cbar.set_ticks(lbls[::-1])
|
||||
ax6.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
Reference in new issue
Block a user