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])