ITAS Automatic Weather Stations
GFI has a set of 6 automatic weather stations composed of robust standard instrumentation. Measured parameters include pressure, air temperature, relative humidity, wind speed and direction, precipitation and global radiation.
In detail, the AWS includes the following instruments:
| Instrument name | Function |
|---|---|
| Campbell Scientific | CR1000 datalogger |
| RM Young 61302V | trykksensor |
| S+S Regeltechnik HTF 100 Pt100 | temperatursensor |
| Vaisala HMP155 | temperatur- og fuktighetssensor |
| RM Young 5106-45 Alpine | vindhastighets- og vindretningssensor |
| Pessl IM523 | nedbørssensor |
| Apogee SP110 | strålingssensor |
The weather station configuration is fully documented in the attached manual:
File:ITAS_GFI_Manual_20160518.pdf (in Norwegian)
The weather stations are configured to acquire 10 min measurements, and transfer the logger files to GFI via mobile network once per day at 11:00 UTC.
Routines are available at GFI to convert from the ASCII format files to netCDF files will full metadata information.
Python routines
Routines for reading raw logger files and processed netcdf files with python are included in the GFPy module [1] utilizing the function GFPy.Met_data.read_single_AWS(filename)
def read_single_AWS(filename):
"""
Read meteorological data from a single Automated weather station (AWS). Depending on the data format
Parameters
==========
filename: str
path and name of file to be read
Returns
=======
station: pandas dataframe
dataframe containing all variables observed by AWS
variables: dictionary
dictionary containing all met variable shortuts and their long names
"""
Matlab routines
reading AWS data from netcdf files
% read_AWS.m
%--------------------------------
% Description
% read AWS data
% station: Station serial number, e.g. 1432
% startdate: datenum of first day
% enddate: optional: datenum of last day
%--------------------------------
% HS, Thu Feb 28 16:30:27 CET 2019
%--------------------------------
function data=read_AWS(station,startdate,varargin)
if nargin==2,
enddate=startdate;
elseif nargin==3,
enddate=varargin{1};
else
error('Wrong number of arguments in read_AWS.m');
end
for dat=startdate:enddate,
% open file and read variables
fname=sprintf('AWS_%4d/CR1000_%4d_Minutt_%s.nc',station,station,datestr(dat,'yyyymmdd'));
ncid=netcdf.open(fname,'NOWRITE');
% check that file exists
if ncid<0,
error('File %s not found.',fname);
end
% get all variable IDs
varids=netcdf.inqVarIDs(ncid);
% loop through all variables
for i=varids,
[varname,xtype,dimids,natts] = netcdf.inqVar(ncid,i);
if dat==startdate,
data.(varname)=netcdf.getVar(ncid,i,'double');
else
data.(varname)=[data.(varname); netcdf.getVar(ncid,i,'double')];
end
end
netcdf.close(ncid);
end
% set missing data if T<-98
midx=find(data.T<-98);
fnames=fields(data);
for f=1:length(fnames),
data.(fnames{f})(midx)=NaN;
end
end
% fin
plotting AWS data from netcdf files
% plot_AWS.m
%--------------------------------
% Description
% read and plot AWS data
% set station serial numbers and
% dates
%--------------------------------
% HS, Thu Feb 28 16:30:27 CET 2019
%--------------------------------
% set station names
station1=1432;
station2=1436;
% set date
dat1=datenum('2019-02-22');
dat2=datenum('2019-02-27');
% get data
data1=read_AWS(station1,dat1,dat2);
data2=read_AWS(station2,dat1,dat2);
% print out available variables
data1
% plot met overview
width=20;
height=30;
handle=figure(1);
set(handle,'color','w')
set(handle,'PaperUnits', 'centimeters');
set(handle,'PaperSize', [width height]);
set(handle,'PaperPosition', [0.5 0.5 width-0.5 height-0.5]);
clf
subplot(4,1,1);
plot(data1.time,data1.T,'r-');
datetick('x');
set(gca,'TickDir','out');
hold on
plot(data2.time,data2.T,'k-');
datetick('x');
set(gca,'TickDir','out');
ylabel('Air temperature / deg C');
subplot(4,1,2);
plot(data1.time,data1.FF,'r-');
datetick('x');
set(gca,'TickDir','out');
hold on
plot(data2.time,data2.FF,'k-');
ylabel('Wind speed / m s-1');
datetick('x');
set(gca,'TickDir','out');
subplot(4,1,3);
plot(data1.time,data1.DD,'r.');
datetick('x');
set(gca,'TickDir','out');
hold on
plot(data2.time,data2.DD,'k.');
datetick('x');
set(gca,'TickDir','out','YLim',[0 360]);
ylabel('Wind direction / deg');
subplot(4,1,4);
plot(data1.time,data1.RH,'r-');
datetick('x');
set(gca,'TickDir','out');
hold on
plot(data2.time,data2.RH,'k-');
datetick('x');
set(gca,'TickDir','out');
ylabel('Relative humidity / %');
% save as figure in png format
print('-dpng','-r150',sprintf('AWS_%s-%s.png',datestr(dat1,'yyyymmdd'),datestr(dat2,'yyyymmdd')));
% fin
reading AWS data from raw logger files
%read_AWS_raw.m
%--------------------------------
% Description
% read and plot AWS data
% from raw logger files
%--------------------------------
% HS, Thu Feb 28 16:30:27 CET 2019
%--------------------------------
clear
% Change to corresponding data directory
cd('../../data/Incoming/')
% Get names of all files of interest
file='CR1000_1436_Minutt.dat';
% read data
data_all = importdata(file);
% Get the specific variables from file
data.P = data_all.data(:,1); % Pressure [hPa]
data.TA = data_all.data(:,2); % Temperature [deg C]
data.RH = data_all.data(:,3); % Relative Humidity [%]
data.WS = data_all.data(:,4); % Wind Speed [m/s]
data.WD = data_all.data(:,5); % Wind Direction [deg]
data.RR = data_all.data(:,6); % Rain Rate [mm]
data.GHI = data_all.data(:,7); % Global Horizontal Irradiation [W/m2]
% Get the timestamps
data.TIME = datenum(data_all.textdata(5:end,1),'yyyy-mm-dd HH:MM:SS');
tstart = datenum(2019,2,22,20,0,0);
tend = datenum(2019,3,3,15,0,0);
% Plot Data of interest
figure(1)
subplot(2,1,1),plot(data.TIME,data.P)
datetick('x')
xlim([tstart,tend])
subplot(2,1,2),plot(data.TIME,data.TA)
ylim([-1,13])
datetick('x')
xlim([tstart,tend])