- Timestamp:
- Oct 30, 2020 12:06:47 PM (4 years ago)
- Location:
- palm/trunk
- Files:
-
- 2 edited
-
SCRIPTS/palm_cvd (modified) (9 diffs)
-
SOURCE/virtual_measurement_mod.f90 (modified) (38 diffs)
Legend:
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palm/trunk/SCRIPTS/palm_cvd
r4758 r4763 26 26 # ----------------- 27 27 # $Id$ 28 # - Check automatically for data organization (stored in subdirectories or not) 29 # - Convert trajectory and timeseriesProfile coordinates into 1-D coordinates 30 # equivalent to timeseries coordiates. This simplifies processing in PALM and 31 # makes the virtual-measurement module also applicable to other campaigns. 32 # 33 # 4758 2020-10-26 13:03:52Z suehring 28 34 # In order to do not omit observations that are on the same site but have different 29 35 # coordinates or feature-types, process all files rather than only one and omit … … 54 60 import os 55 61 import numpy as np 62 import time 56 63 57 64 … … 187 194 name_featuretype = "featureType" 188 195 name_ts = "timeSeries" 196 name_tspr = "timeSeriesProfile" 189 197 name_traj = "trajectory" 190 198 name_ntime = "ntime" … … 196 204 name_eutm = "E_UTM" 197 205 name_nutm = "N_UTM" 198 name_hao = "height _above_origin"206 name_hao = "height" 199 207 name_station_h = "station_h" 200 208 name_z = "z" … … 271 279 if ( input_from_observations ): 272 280 273 # Run loop over all subdirectories, detect the files and extract a list of sites. 281 # Run loop over all listed input data. Depending on the data set, this could be 282 # a list of files or a list of subdirectories. 274 283 # This is done to reduce the number of virtual measurements in the model. Each 275 284 # virtual measurement has an overhead and consumes memory. 276 285 sites = [] 277 286 input_files = [] 287 input_files_orig = [] 278 288 for dirname in list_input_data: 279 289 data_file = data_path + dirname 280 290 281 # Directory may contain various file versions. 282 # Take the one with highest cycle number. 283 highest_cycle_nr = 0 284 for filename in os.listdir(data_file): 285 start_seq = len( filename ) - 6 286 end_seq = len( filename ) - 3 287 if int( filename[start_seq:end_seq] ) > highest_cycle_nr: 288 highest_cycle_nr = int(filename[start_seq:end_seq]) 289 latest_file = filename 291 if ( os.path.isdir(data_file) == True ): 292 # Directory may contain various file versions. 293 # Take the one with highest cycle number. 294 highest_cycle_nr = 0 295 for filename in os.listdir(data_file): 296 start_seq = len( filename ) - 6 297 end_seq = len( filename ) - 3 298 if int( filename[start_seq:end_seq] ) > highest_cycle_nr: 299 highest_cycle_nr = int(filename[start_seq:end_seq]) 300 latest_file = filename 301 input_file = data_file + "/" + latest_file 302 input_file_orig = latest_file 303 else: 304 input_file = data_file 305 input_file_orig = dirname 290 306 291 307 # Open the NetCDF file 292 input_file = data_file + "/" + latest_file293 308 ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii') 294 309 input_files.append(input_file) 295 296 # Define a nested list of default variables that shall be measured. Based on this list, 297 # the final number of measured variables is determined. 298 measured_variables_all_sites = [ ['u', 'v', 'w', 'theta', 'hus'] for var in range(0, len(input_files))]299 300 # Run loop over all subdirectories that contain observational data301 for counter, file in enumerate(input_files):302 303 # Open the NetCDF input file304 input_file = input_files[counter]310 input_files_orig.append(input_file_orig) 311 312 # Gather all files according to their feature type and all sites for the respective feature type 313 files_traj = [] 314 files_ts = [] 315 files_tspr = [] 316 sites_traj = [] 317 sites_ts = [] 318 sites_tspr = [] 319 for input_file in input_files: 305 320 ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) 306 321 307 # Determine index for the treated site308 num_vmeas = input_files.index( input_file ) + 1309 print( counter, num_vmeas )310 311 # Read global attributes and write them immediately into the output file312 322 for att in ncfile_in.ncattrs(): 313 323 if ( att == name_featuretype ): 314 324 feature = ncfile_in.getncattr(att) 315 if ( att == name_datacontent ):316 content = ncfile_in.getncattr(att)317 325 if ( att == name_site ): 318 326 site = ncfile_in.getncattr(att) 319 327 320 if ( att in atts_float ): 321 ncfile_out.setncattr( att + str(num_vmeas), np.double(ncfile_in.getncattr(att)) ) 322 else: 323 ncfile_out.setncattr( att + str(num_vmeas), ncfile_in.getncattr(att) ) 324 325 #timeSeries 326 if ( feature == name_ts ): 327 ntime = len( ncfile_in.dimensions[name_ntime] ) 328 nstat = len( ncfile_in.dimensions[name_station] ) 329 ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) 330 ncfile_out.createDimension( name_station + str(num_vmeas), nstat ) 331 332 #trajectory 333 elif ( feature == name_traj ): 334 ntime = len( ncfile_in.dimensions[name_ntime] ) 335 ntraj = len( ncfile_in.dimensions[name_traj_dim] ) 336 ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) 337 ncfile_out.createDimension( name_traj_dim + str(num_vmeas), ntraj ) 338 339 #timeseriesProfile 328 if ( feature == name_traj ): 329 files_traj.append(input_file) 330 elif ( feature == name_ts ): 331 files_ts.append(input_file) 340 332 else: 341 ntime = len( ncfile_in.dimensions[name_ntime] ) 342 nstat = len( ncfile_in.dimensions[name_station] ) 343 nz = len( ncfile_in.dimensions[name_nz] ) 344 ncfile_out.createDimension( name_ntime + str(num_vmeas), ntime ) 345 ncfile_out.createDimension( name_station + str(num_vmeas), nstat ) 346 ncfile_out.createDimension( name_nz + str(num_vmeas), nz ) 347 348 for var in ncfile_in.variables.keys(): 349 if ( var in dims_out ): 350 # Create a variable and write it to file after it is read. In order to 351 # avoid fill values in the dimensions, these are converted to zero 352 # before written to file. Depending on the featureType of the measurement, 353 # the array shape is different. For more informations, please see 354 # [UC]2 data standard. 355 # Timeseries 356 if ( feature == name_ts ): 357 temp_ts = ncfile_out.createVariable( var + str(num_vmeas), float, \ 358 name_station + str(num_vmeas)) 359 temp_ts[:] = np.nan_to_num( ncfile_in.variables[var][:] ) 360 361 # Trajectories 362 elif ( feature == name_traj ): 363 # @note: If there are files where station_h is present although featureType is 364 # trajectory, station_h must not be read. 365 if var != name_station_h: 366 temp_traj = ncfile_out.createVariable( var + str(num_vmeas), float, \ 367 ( name_traj_dim + str(num_vmeas), \ 368 name_ntime + str(num_vmeas) ) ) 369 temp_traj[:,:] = np.nan_to_num( ncfile_in.variables[var][:,:] ) 370 371 # TimeseriesProfiles 372 else: 373 if ( var == 'z' ): 374 temp_pr = ncfile_out.createVariable( var + str(num_vmeas), float, \ 375 ( name_station + str(num_vmeas), \ 376 name_nz + str(num_vmeas) ) ) 377 temp_pr[:] = np.nan_to_num( ncfile_in.variables[var][:,0,:] ) 378 else: 379 temp_pr = ncfile_out.createVariable( var + str(num_vmeas), float, \ 380 name_station + str(num_vmeas)) 381 temp_pr[:] = np.nan_to_num( ncfile_in.variables[var][:] ) 382 383 # Search for variables to be measured. In case the variable isn't already defined, 384 # append the variable to the list. 385 for var in ncfile_in.variables.keys(): 386 if ( var not in non_measurable_vars and \ 387 var not in vars_default and \ 388 var not in measured_variables_all_sites[input_files.index( input_file )] ): 389 390 measured_variables_all_sites[input_files.index( input_file )].append(var) 391 392 # Close the NetCDF input file 333 files_tspr.append(input_file) 334 335 if ( feature == name_traj and site not in sites_traj ): 336 sites_traj.append(site) 337 if ( feature == name_ts and site not in sites_ts ): 338 sites_ts.append(site) 339 if ( feature == name_tspr and site not in sites_tspr ): 340 sites_tspr.append(site) 341 393 342 ncfile_in.close() 394 343 395 # After variables are gathered and dimensions / attributes are already written to file, 396 # the list of measured variables is written to file. 397 for site in input_files: 398 399 num_vmeas = input_files.index( site ) + 1 400 401 ncfile_out.createDimension( "nvar"+ str(num_vmeas), \ 402 len( measured_variables_all_sites[input_files.index( site )] ) ) 403 404 measured = ncfile_out.createVariable( 'measured_variables' + str(num_vmeas), 'S1', \ 405 ("nvar" + str(num_vmeas), "string_len")) # must be NC_CHAR 406 407 for counter, meas in enumerate( measured_variables_all_sites[input_files.index( site )] ): 408 measured[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) 409 410 # Check if any of the measured variables is a soil variable. Set flag accordingly. 344 for input_file in files_traj: 345 print( "traj", input_file ) 346 for site in sites_traj: 347 print( "traj", site ) 348 349 for site in sites_ts: 350 print( "ts", site ) 351 for site in sites_tspr: 352 print( "tspr", site ) 353 354 for file in files_tspr: 355 print( "tspr", file ) 356 counter_id = 1 357 for site_traj in sites_traj: 358 # For the given site already define the featureTpye and site 359 ncfile_out.setncattr( name_featuretype + str(counter_id), name_traj ) 360 ncfile_out.setncattr( name_site + str(counter_id), site_traj ) 361 362 # Define the number of coordinates for the site 363 num_coords = 0 364 365 e_utm_traj = np.array([]) 366 n_utm_traj = np.array([]) 367 h_traj = np.array([]) 368 measured_variables = ['u', 'v', 'w', 'theta', 'hus'] 369 for input_file in files_traj: 370 ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) 371 for att in ncfile_in.ncattrs(): 372 if ( att == name_site ): 373 site = ncfile_in.getncattr(att) 374 375 if ( site == site_traj ): 376 377 for att in ncfile_in.ncattrs(): 378 if ( att == name_orig_x ): 379 orig_x = ncfile_in.getncattr(att) 380 if ( att == name_orig_y ): 381 orig_y = ncfile_in.getncattr(att) 382 if ( att == name_orig_z ): 383 orig_z = ncfile_in.getncattr(att) 384 385 ntime = len( ncfile_in.dimensions[name_ntime] ) 386 ntraj = len( ncfile_in.dimensions[name_traj_dim] ) 387 388 num_coords += ntime * ntraj 389 # Gather UTM and height coordinates and merge them into one array. Further, gather 390 # the variables that shall be sampled. Coordinates are checked to for NaN values and 391 # are tranformed to arithmetric numbers. Further, 2D input array is transformed into 392 # a 1D array. 393 for var in ncfile_in.variables.keys(): 394 if ( var in dims_out and var == name_eutm ): 395 e_utm_traj = np.append(e_utm_traj, np.nan_to_num( ncfile_in.variables[var][:,:] ).flatten()) 396 #e_utm_traj.append( np.nan_to_num( ncfile_in.variables[var][:,:] ).flatten() ) 397 if ( var in dims_out and var == name_nutm ): 398 n_utm_traj = np.append(n_utm_traj, np.nan_to_num( ncfile_in.variables[var][:,:] ).flatten()) 399 if ( var in dims_out and var == name_hao ): 400 h_traj = np.append(h_traj, np.nan_to_num( ncfile_in.variables[var][:,:] ).flatten()) 401 402 if ( var not in non_measurable_vars and \ 403 var not in vars_default and \ 404 var not in measured_variables ): 405 measured_variables.append(var) 406 407 ncfile_in.close() 408 409 # After all files for the current site are processed, write the origin-coordinates for x,y,z 410 ncfile_out.setncattr( name_orig_x + str(counter_id), orig_x ) 411 ncfile_out.setncattr( name_orig_y + str(counter_id), orig_y ) 412 ncfile_out.setncattr( name_orig_z + str(counter_id), orig_z ) 413 # Create the dimensions 414 ncfile_out.createDimension( name_station + str(counter_id), num_coords ) 415 416 temp_traj = ncfile_out.createVariable( name_eutm + str(counter_id), float, name_station + str(counter_id) ) 417 temp_traj[:] = e_utm_traj 418 419 temp_traj = ncfile_out.createVariable( name_nutm + str(counter_id), float, name_station + str(counter_id) ) 420 temp_traj[:] = n_utm_traj 421 422 temp_traj = ncfile_out.createVariable( name_hao + str(counter_id), float, name_station + str(counter_id) ) 423 temp_traj[:] = h_traj 424 425 # Check if any of the measured variables is a soil variable. Set flag accordingly. 411 426 soil = False 412 for var in measured_variables _all_sites[input_files.index( site )]:427 for var in measured_variables: 413 428 if ( var in soil_vars ): 414 429 soil = True 415 416 # Write soil flag 417 ncfile_out.setncattr( name_soil_sampling + str( num_vmeas), np.int8(soil) ) 418 419 420 421 # Store the number of observational sites 422 num_sites += len( input_files ) 430 # Write soil flag 431 ncfile_out.setncattr( name_soil_sampling + str( counter_id), np.int8(soil) ) 432 433 # Create dimension for sample-variable string 434 ncfile_out.createDimension( "nvar"+ str(counter_id), len( measured_variables ) ) 435 436 measured_var = ncfile_out.createVariable( 'measured_variables' + str(counter_id), 'S1', \ 437 ("nvar" + str(counter_id), "string_len") ) # must be NC_CHAR 438 439 # Write the variables to the file 440 for counter, meas in enumerate( measured_variables ): 441 measured_var[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) 442 443 # Increment the counter 444 counter_id += 1 445 446 447 for site_tspr in sites_tspr: 448 # For the given site already define the featureTpye and site 449 ncfile_out.setncattr( name_featuretype + str(counter_id), name_tspr ) 450 ncfile_out.setncattr( name_site + str(counter_id), site_tspr ) 451 452 # Define the number of coordinates for the site 453 num_coords = 0 454 e_utm_tspr = np.array([]) 455 n_utm_tspr = np.array([]) 456 station_h_tspr = np.array([]) 457 z_tspr = np.array([]) 458 459 measured_variables = ['u', 'v', 'w', 'theta', 'hus'] 460 for input_file in files_tspr: 461 ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) 462 for att in ncfile_in.ncattrs(): 463 if ( att == name_site ): 464 site = ncfile_in.getncattr(att) 465 466 if ( site == site_tspr ): 467 for att in ncfile_in.ncattrs(): 468 if ( att == name_orig_x ): 469 orig_x = ncfile_in.getncattr(att) 470 if ( att == name_orig_y ): 471 orig_y = ncfile_in.getncattr(att) 472 if ( att == name_orig_z ): 473 orig_z = ncfile_in.getncattr(att) 474 475 nstation = len( ncfile_in.dimensions[name_station] ) 476 ntime = len( ncfile_in.dimensions[name_ntime] ) 477 nz = len( ncfile_in.dimensions[name_nz] ) 478 479 num_coords += nstation * ntime * nz 480 # Gather UTM and height coordinates and merge them into one array. Further, gather 481 # the variables that shall be sampled. Coordinates are checked to for NaN values and 482 # are tranformed to arithmetric numbers. Further, 2D input array is transformed into 483 # a 1D array. 484 for var in ncfile_in.variables.keys(): 485 tspr_tmp1 = np.zeros((nstation)) 486 tspr_tmp2 = np.zeros((ntime*nz)) 487 if ( var in dims_out and var == name_eutm ): 488 tspr_tmp1 = np.nan_to_num( ncfile_in.variables[var][:] ) 489 for ns in range(0,int(nstation)): 490 tspr_tmp2[:] = tspr_tmp1[ns] 491 e_utm_tspr = np.append(e_utm_tspr, tspr_tmp2) 492 if ( var in dims_out and var == name_nutm ): 493 tspr_tmp1 = np.nan_to_num( ncfile_in.variables[var][:] ) 494 for ns in range(0,int(nstation)): 495 tspr_tmp2[:] = tspr_tmp1[ns] 496 n_utm_tspr = np.append(n_utm_tspr, tspr_tmp2) 497 if ( var in dims_out and var == name_z ): 498 z_tspr_tmp = np.nan_to_num( ncfile_in.variables[var][:,:,:] ) 499 z_tspr = np.append(z_tspr, np.concatenate( z_tspr_tmp )) 500 if ( var in dims_out and var == name_station_h ): 501 tspr_tmp1 = np.nan_to_num( ncfile_in.variables[var][:] ) 502 for ns in range(0,int(nstation)): 503 tspr_tmp2[:] = tspr_tmp1[ns] 504 station_h_tspr = np.append(station_h_tspr, tspr_tmp2) 505 506 if ( var not in non_measurable_vars and \ 507 var not in vars_default and \ 508 var not in measured_variables ): 509 measured_variables.append(var) 510 511 ncfile_in.close() 512 513 # After all files for the current site are processed, write the origin-coordinates for x,y,z 514 ncfile_out.setncattr( name_orig_x + str(counter_id), orig_x ) 515 ncfile_out.setncattr( name_orig_y + str(counter_id), orig_y ) 516 ncfile_out.setncattr( name_orig_z + str(counter_id), orig_z ) 517 # Create the dimensions 518 ncfile_out.createDimension( name_station + str(counter_id), num_coords ) 519 520 temp_tspr = ncfile_out.createVariable( name_eutm + str(counter_id), float, name_station + str(counter_id) ) 521 temp_tspr[:] = e_utm_tspr 522 523 temp_tspr = ncfile_out.createVariable( name_nutm + str(counter_id), float, name_station + str(counter_id) ) 524 temp_tspr[:] = n_utm_tspr 525 526 temp_tspr = ncfile_out.createVariable( name_z + str(counter_id), float, name_station + str(counter_id) ) 527 temp_tspr[:] = z_tspr 528 529 temp_tspr = ncfile_out.createVariable( name_station_h + str(counter_id), float, name_station + str(counter_id) ) 530 temp_tspr[:] = station_h_tspr 531 532 # Check if any of the measured variables is a soil variable. Set flag accordingly. 533 soil = False 534 for var in measured_variables: 535 if ( var in soil_vars ): 536 soil = True 537 # Write soil flag 538 ncfile_out.setncattr( name_soil_sampling + str( counter_id), np.int8(soil) ) 539 540 # Create dimension for sample-variable string 541 ncfile_out.createDimension( "nvar"+ str(counter_id), len( measured_variables ) ) 542 543 measured_var = ncfile_out.createVariable( 'measured_variables' + str(counter_id), 'S1', \ 544 ("nvar" + str(counter_id), "string_len") ) # must be NC_CHAR 545 546 # Write the variables to the file 547 for counter, meas in enumerate( measured_variables ): 548 measured_var[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) 549 550 # Increment the counter 551 counter_id += 1 552 553 554 for site_ts in sites_ts: 555 # For the given site already define the featureTpye and site 556 ncfile_out.setncattr( name_featuretype + str(counter_id), name_ts ) 557 ncfile_out.setncattr( name_site + str(counter_id), site_ts ) 558 559 # Define the number of coordinates for the site 560 num_coords = 0 561 e_utm_ts = np.array([]) 562 n_utm_ts = np.array([]) 563 station_h_ts = np.array([]) 564 z_ts = np.array([]) 565 566 measured_variables = ['u', 'v', 'w', 'theta', 'hus'] 567 for input_file in files_ts: 568 ncfile_in = Dataset( input_file, "r", format="NETCDF4", encoding='ascii' ) 569 for att in ncfile_in.ncattrs(): 570 if ( att == name_site ): 571 site = ncfile_in.getncattr(att) 572 573 if ( site == site_ts ): 574 575 for att in ncfile_in.ncattrs(): 576 if ( att == name_orig_x ): 577 orig_x = ncfile_in.getncattr(att) 578 if ( att == name_orig_y ): 579 orig_y = ncfile_in.getncattr(att) 580 if ( att == name_orig_z ): 581 orig_z = ncfile_in.getncattr(att) 582 583 nstation = len( ncfile_in.dimensions[name_station] ) 584 num_coords += nstation 585 # Gather UTM and height coordinates and merge them into one array. Further, gather 586 # the variables that shall be sampled. Coordinates are checked to for NaN values and 587 # are tranformed to arithmetric numbers. 588 for var in ncfile_in.variables.keys(): 589 if ( var in dims_out and var == name_eutm ): 590 e_utm_ts = np.append(e_utm_ts, np.nan_to_num( ncfile_in.variables[var][:] )) 591 if ( var in dims_out and var == name_nutm ): 592 n_utm_ts = np.append(n_utm_ts, np.nan_to_num( ncfile_in.variables[var][:] )) 593 if ( var in dims_out and var == name_z ): 594 z_ts = np.append(z_ts, np.nan_to_num( ncfile_in.variables[var][:] )) 595 if ( var in dims_out and var == name_station_h ): 596 station_h_ts = np.append(station_h_ts, np.nan_to_num( ncfile_in.variables[var][:] )) 597 598 if ( var not in non_measurable_vars and \ 599 var not in vars_default and \ 600 var not in measured_variables ): 601 measured_variables.append(var) 602 603 ncfile_in.close() 604 605 # After all files for the current site are processed, write the origin-coordinates for x,y,z 606 ncfile_out.setncattr( name_orig_x + str(counter_id), orig_x ) 607 ncfile_out.setncattr( name_orig_y + str(counter_id), orig_y ) 608 ncfile_out.setncattr( name_orig_z + str(counter_id), orig_z ) 609 # Create the dimensions 610 ncfile_out.createDimension( name_station + str(counter_id), num_coords ) 611 612 temp_ts = ncfile_out.createVariable( name_eutm + str(counter_id), float, name_station + str(counter_id) ) 613 temp_ts[:] = e_utm_ts 614 615 temp_ts = ncfile_out.createVariable( name_nutm + str(counter_id), float, name_station + str(counter_id) ) 616 temp_ts[:] = n_utm_ts 617 618 temp_ts = ncfile_out.createVariable( name_z + str(counter_id), float, name_station + str(counter_id) ) 619 temp_ts[:] = z_ts 620 621 temp_ts = ncfile_out.createVariable( name_station_h + str(counter_id), float, name_station + str(counter_id) ) 622 temp_ts[:] = station_h_ts 623 624 # Check if any of the measured variables is a soil variable. Set flag accordingly. 625 soil = False 626 for var in measured_variables: 627 if ( var in soil_vars ): 628 soil = True 629 # Write soil flag 630 ncfile_out.setncattr( name_soil_sampling + str( counter_id), np.int8(soil) ) 631 632 # Create dimension for sample-variable string 633 ncfile_out.createDimension( "nvar"+ str(counter_id), len( measured_variables ) ) 634 635 measured_var = ncfile_out.createVariable( 'measured_variables' + str(counter_id), 'S1', \ 636 ("nvar" + str(counter_id), "string_len") ) # must be NC_CHAR 637 638 # Write the variables to the file 639 for counter, meas in enumerate( measured_variables ): 640 measured_var[counter] = stringtochar( np.array( meas,"S%s"%(max_string_len) ) ) 641 642 # Increment the counter 643 counter_id += 1 644 645 # Store the number of observational sites 646 num_sites = len( sites_traj ) + len( sites_ts ) + len( sites_tspr ) 423 647 424 648 … … 426 650 # are possible. 427 651 if ( custom_coordinates ): 428 429 count_site = num_sites + 1 652 num_sites = counter_id - 1 430 653 for coord in coordinates: 431 654 # Define mandatory attributes 432 ncfile_out.setncattr( name_featuretype + str(count _site), \655 ncfile_out.setncattr( name_featuretype + str(counter_id), \ 433 656 name_ts ) 434 ncfile_out.setncattr( name_site + str(count _site), \435 "custom" + str(count _site- num_sites) )436 ncfile_out.setncattr( name_orig_x + str(count _site), \657 ncfile_out.setncattr( name_site + str(counter_id), \ 658 "custom" + str(counter_id - num_sites) ) 659 ncfile_out.setncattr( name_orig_x + str(counter_id), \ 437 660 coord[0] ) 438 ncfile_out.setncattr( name_orig_y + str(count _site), \661 ncfile_out.setncattr( name_orig_y + str(counter_id), \ 439 662 coord[1] ) 440 ncfile_out.setncattr( name_orig_z + str(count _site), \663 ncfile_out.setncattr( name_orig_z + str(counter_id), \ 441 664 0.0 ) 442 665 … … 444 667 ntime = 1 445 668 nstat = 1 446 ncfile_out.createDimension( name_ntime + str(count _site), ntime )447 ncfile_out.createDimension( name_station + str(count _site), nstat )669 ncfile_out.createDimension( name_ntime + str(counter_id), ntime ) 670 ncfile_out.createDimension( name_station + str(counter_id), nstat ) 448 671 449 672 # Define coordinate variables 450 temp_ts = ncfile_out.createVariable( name_eutm + str(count _site), \673 temp_ts = ncfile_out.createVariable( name_eutm + str(counter_id), \ 451 674 float, \ 452 name_station + str(count _site) )675 name_station + str(counter_id) ) 453 676 temp_ts[:] = np.array( coord[0] ) 454 677 455 temp_ts = ncfile_out.createVariable( name_nutm + str(count _site), \678 temp_ts = ncfile_out.createVariable( name_nutm + str(counter_id), \ 456 679 float, \ 457 name_station + str(count _site) )680 name_station + str(counter_id) ) 458 681 temp_ts[:] = np.array( coord[1] ) 459 682 460 temp_ts = ncfile_out.createVariable( name_z + str(count _site), \683 temp_ts = ncfile_out.createVariable( name_z + str(counter_id), \ 461 684 float, \ 462 name_station + str(count _site) )685 name_station + str(counter_id) ) 463 686 temp_ts[:] = np.array( coord[2] ) 464 687 465 temp_ts = ncfile_out.createVariable( name_station_h + str(count _site), \688 temp_ts = ncfile_out.createVariable( name_station_h + str(counter_id), \ 466 689 float, \ 467 name_station + str(count _site) )690 name_station + str(counter_id) ) 468 691 temp_ts[:] = np.array( 0.0 ) 469 692 470 693 471 count _site+= 1694 counter_id += 1 472 695 473 696 # Reset counter variable 474 count _site= num_sites + 1697 counter_id = num_sites + 1 475 698 476 699 # check if variables are prescribed. If so, prepare final output string … … 491 714 measured_variables.append(var) 492 715 493 ncfile_out.createDimension( "nvar"+ str(count _site), \716 ncfile_out.createDimension( "nvar"+ str(counter_id), \ 494 717 len( measured_variables ) ) 495 718 496 measured_var = ncfile_out.createVariable( 'measured_variables' + str(count _site), 'S1', \497 ("nvar" + str(count _site), "string_len") ) # must be NC_CHAR719 measured_var = ncfile_out.createVariable( 'measured_variables' + str(counter_id), 'S1', \ 720 ("nvar" + str(counter_id), "string_len") ) # must be NC_CHAR 498 721 499 722 # Write the variables to the file … … 507 730 508 731 # Write soil flag 509 ncfile_out.setncattr( name_soil_sampling + str( count _site), np.int8(soil) )732 ncfile_out.setncattr( name_soil_sampling + str( counter_id), np.int8(soil) ) 510 733 511 734 # Increment counter variable 512 count _site+= 1735 counter_id += 1 513 736 514 737 del ( measured_variables[:] ) 515 738 516 739 # Add the number of customized sites. 517 num_sites += int( number_positions )740 num_sites = counter_id - 1 518 741 519 742 -
palm/trunk/SOURCE/virtual_measurement_mod.f90
r4752 r4763 25 25 ! ----------------- 26 26 ! $Id$ 27 ! - Simplified input of coordinates. All coordinate and height arrays are now 1D, independent on 28 ! featuretype. This allows easier usage also for other campaign data sets independent of UC2. 29 ! - Avoid type conversion from 64 to 32 bit when output the data 30 ! - Increase dimension size of sample-variable string (sometimes more than 100 variables are listed 31 ! for heavy measured locations) 32 ! - Remove quantities that cannot be sampled 33 ! 34 ! 4752 2020-10-21 13:54:51Z suehring 27 35 ! Remove unnecessary output and merge output for quantities that are represented by the same 28 36 ! variable in PALM, e.g. surface temperature and brightness temperature … … 191 199 ONLY: air_chemistry, & 192 200 coupling_char, & 201 debug_output, & 193 202 dz, & 194 203 end_time, & … … 305 314 INTEGER(iwp) :: ns = 0 !< number of observation coordinates on subdomain, for atmospheric measurements 306 315 INTEGER(iwp) :: ns_tot = 0 !< total number of observation coordinates, for atmospheric measurements 307 INTEGER(iwp) :: n _tr_st !< number of trajectories / station ofa measurement316 INTEGER(iwp) :: nst !< number of coordinate points given for a measurement 308 317 INTEGER(iwp) :: nmeas !< number of measured variables (atmosphere + soil) 309 318 INTEGER(iwp) :: ns_soil = 0 !< number of observation coordinates on subdomain, for soil measurements … … 677 686 output_variable%units = 'm2 s-2' 678 687 679 CASE ( 'plev' )680 output_variable%long_name = 'air pressure'681 output_variable%standard_name = 'air_pressure'682 output_variable%units = 'Pa'683 684 688 CASE ( 'm_soil' ) 685 689 output_variable%long_name = 'soil moisture volumetric' … … 731 735 output_variable%units = 'hPa' 732 736 733 CASE ( 'pswrtg' )734 output_variable%long_name = 'platform speed wrt ground'735 output_variable%standard_name = 'platform_speed_wrt_ground'736 output_variable%units = 'm s-1'737 738 CASE ( 'pswrta' )739 output_variable%long_name = 'platform speed wrt air'740 output_variable%standard_name = 'platform_speed_wrt_air'741 output_variable%units = 'm s-1'742 743 737 CASE ( 'pwv' ) 744 738 output_variable%long_name = 'water vapor partial pressure in air' … … 746 740 output_variable%units = 'hPa' 747 741 748 CASE ( 'ssdu' )749 output_variable%long_name = 'duration of sunshine'750 output_variable%standard_name = 'duration_of_sunshine'751 output_variable%units = 's'752 753 742 CASE ( 't_lw' ) 754 743 output_variable%long_name = 'land water temperature' … … 771 760 output_variable%long_name = 'upward kinematic latent heat flux in air' 772 761 output_variable%units = 'g kg-1 m s-1' 773 774 CASE ( 'zcb' )775 output_variable%long_name = 'cloud base altitude'776 output_variable%standard_name = 'cloud_base_altitude'777 output_variable%units = 'm'778 779 CASE ( 'zmla' )780 output_variable%long_name = 'atmosphere boundary layer thickness'781 output_variable%standard_name = 'atmosphere_boundary_layer_thickness'782 output_variable%units = 'm'783 762 784 763 CASE ( 'mcpm1' ) … … 880 859 881 860 CHARACTER(LEN=5) :: dum !< dummy string indicating station id 882 CHARACTER(LEN=100), DIMENSION(50) :: measured_variables_file = '' !< array with all measured variables read from NetCDF 883 CHARACTER(LEN=100), DIMENSION(50) :: measured_variables = '' !< dummy array with all measured variables that are allowed 884 885 INTEGER(iwp) :: dim_ntime !< dimension size of time coordinate 861 CHARACTER(LEN=100), DIMENSION(200) :: measured_variables_file = '' !< array with all measured variables read from NetCDF 862 CHARACTER(LEN=100), DIMENSION(200) :: measured_variables = '' !< dummy array with all measured variables that are allowed 863 886 864 INTEGER(iwp) :: i !< grid index of virtual observation point in x-direction 887 865 INTEGER(iwp) :: is !< grid index of real observation point of the respective station in x-direction … … 897 875 INTEGER(iwp) :: ll !< running index over all measured variables in file 898 876 INTEGER(iwp) :: m !< running index for surface elements 899 INTEGER(iwp) :: n !< running index over trajectory coordinates900 877 INTEGER(iwp) :: nofill !< dummy for nofill return value (not used) 901 878 INTEGER(iwp) :: ns !< counter variable for number of observation points on subdomain … … 922 899 REAL(wp) :: fill_zar !< _FillValue for zar coordinate 923 900 924 REAL(wp), DIMENSION(:,:), ALLOCATABLE :: e_utm !< easting UTM coordinate, temporary variable 925 REAL(wp), DIMENSION(:,:), ALLOCATABLE :: e_utm_tmp !< EUTM coordinate before rotation 926 REAL(wp), DIMENSION(:,:), ALLOCATABLE :: height !< observation height above ground (for trajectories) 927 REAL(wp), DIMENSION(:,:), ALLOCATABLE :: n_utm !< northing UTM coordinate, temporary variable 928 REAL(wp), DIMENSION(:,:), ALLOCATABLE :: n_utm_tmp !< NUTM coordinate before rotation 929 REAL(wp), DIMENSION(:,:), ALLOCATABLE :: station_h !< station height above reference 930 REAL(wp), DIMENSION(:,:), ALLOCATABLE :: zar !< observation height above reference 901 REAL(wp), DIMENSION(:), ALLOCATABLE :: e_utm !< easting UTM coordinate, temporary variable 902 REAL(wp), DIMENSION(:), ALLOCATABLE :: e_utm_tmp !< EUTM coordinate before rotation 903 REAL(wp), DIMENSION(:), ALLOCATABLE :: height !< observation height above ground (for trajectories) 904 REAL(wp), DIMENSION(:), ALLOCATABLE :: n_utm !< northing UTM coordinate, temporary variable 905 REAL(wp), DIMENSION(:), ALLOCATABLE :: n_utm_tmp !< NUTM coordinate before rotation 906 REAL(wp), DIMENSION(:), ALLOCATABLE :: station_h !< station height above reference 907 REAL(wp), DIMENSION(:), ALLOCATABLE :: zar !< observation height above reference 908 909 IF ( debug_output ) CALL debug_message( 'vm_init', 'start' ) 931 910 #if defined( __netcdf ) 932 911 ! … … 1042 1021 IF ( vmea(l)%nmeas > 0 ) THEN 1043 1022 ! 1044 !-- For stationary measurements UTM coordinates are just one value and its dimension is 1045 !-- "station", while for mobile measurements UTM coordinates are arrays depending on the 1046 !-- number of trajectories and time, according to (UC)2 standard. First, inquire dimension 1047 !-- length of the UTM coordinates. 1048 IF ( vmea(l)%trajectory ) THEN 1049 ! 1050 !-- For non-stationary measurements read the number of trajectories and the number of time 1051 !-- coordinates. 1052 CALL get_dimension_length( pids_id, vmea(l)%n_tr_st, "traj" // TRIM( dum ) ) 1053 CALL get_dimension_length( pids_id, dim_ntime, "ntime" // TRIM( dum ) ) 1054 ! 1055 !-- For stationary measurements the dimension for UTM is station and for the time-coordinate 1056 !-- it is one. 1057 ELSE 1058 CALL get_dimension_length( pids_id, vmea(l)%n_tr_st, "station" // TRIM( dum ) ) 1059 dim_ntime = 1 1060 ENDIF 1061 ! 1062 !- Allocate array which defines individual time/space frame for each trajectory or station. 1063 ALLOCATE( vmea(l)%dim_t(1:vmea(l)%n_tr_st) ) 1023 !-- For stationary and mobile measurements UTM coordinates are just one value and its dimension 1024 !-- is "station". 1025 CALL get_dimension_length( pids_id, vmea(l)%nst, "station" // TRIM( dum ) ) 1064 1026 ! 1065 1027 !-- Allocate temporary arrays for UTM and height coordinates. Note, on file UTM coordinates 1066 1028 !-- might be 1D or 2D variables 1067 ALLOCATE( e_utm(1:vmea(l)%n _tr_st,1:dim_ntime) )1068 ALLOCATE( n_utm(1:vmea(l)%n _tr_st,1:dim_ntime) )1069 ALLOCATE( station_h(1:vmea(l)%n _tr_st,1:dim_ntime) )1070 ALLOCATE( zar(1:vmea(l)%n _tr_st,1:dim_ntime) )1071 IF ( vmea(l)%trajectory ) ALLOCATE( height(1:vmea(l)%n _tr_st,1:dim_ntime) )1029 ALLOCATE( e_utm(1:vmea(l)%nst) ) 1030 ALLOCATE( n_utm(1:vmea(l)%nst) ) 1031 ALLOCATE( station_h(1:vmea(l)%nst) ) 1032 ALLOCATE( zar(1:vmea(l)%nst) ) 1033 IF ( vmea(l)%trajectory ) ALLOCATE( height(1:vmea(l)%nst) ) 1072 1034 e_utm = 0.0_wp 1073 1035 n_utm = 0.0_wp … … 1076 1038 IF ( vmea(l)%trajectory ) height = 0.0_wp 1077 1039 1078 ALLOCATE( e_utm_tmp(1:vmea(l)%n _tr_st,1:dim_ntime) )1079 ALLOCATE( n_utm_tmp(1:vmea(l)%n _tr_st,1:dim_ntime) )1040 ALLOCATE( e_utm_tmp(1:vmea(l)%nst) ) 1041 ALLOCATE( n_utm_tmp(1:vmea(l)%nst) ) 1080 1042 ! 1081 1043 !-- Read UTM and height coordinates for all trajectories and times. Note, in case … … 1094 1056 !-- UC2-standard. 1095 1057 IF ( vmea(l)%trajectory ) THEN 1096 CALL get_variable( pids_id, char_eutm // TRIM( dum ), e_utm, 0, dim_ntime-1, 0, & 1097 vmea(l)%n_tr_st-1 ) 1098 CALL get_variable( pids_id, char_nutm // TRIM( dum ), n_utm, 0, dim_ntime-1, 0, & 1099 vmea(l)%n_tr_st-1 ) 1100 CALL get_variable( pids_id, char_zar // TRIM( dum ), zar, 0, dim_ntime-1, 0, & 1101 vmea(l)%n_tr_st-1 ) 1102 CALL get_variable( pids_id, char_height // TRIM( dum ), height, 0, dim_ntime-1, 0, & 1103 vmea(l)%n_tr_st-1 ) 1058 CALL get_variable( pids_id, char_eutm // TRIM( dum ), e_utm(:) ) 1059 CALL get_variable( pids_id, char_nutm // TRIM( dum ), n_utm(:) ) 1060 CALL get_variable( pids_id, char_height // TRIM( dum ), height(:) ) 1104 1061 ELSE 1105 CALL get_variable( pids_id, char_eutm // TRIM( dum ), e_utm(: ,1) )1106 CALL get_variable( pids_id, char_nutm // TRIM( dum ), n_utm(: ,1) )1107 CALL get_variable( pids_id, char_station_h // TRIM( dum ), station_h(: ,1) )1108 CALL get_variable( pids_id, char_zar // TRIM( dum ), zar(: ,1) )1062 CALL get_variable( pids_id, char_eutm // TRIM( dum ), e_utm(:) ) 1063 CALL get_variable( pids_id, char_nutm // TRIM( dum ), n_utm(:) ) 1064 CALL get_variable( pids_id, char_station_h // TRIM( dum ), station_h(:) ) 1065 CALL get_variable( pids_id, char_zar // TRIM( dum ), zar(:) ) 1109 1066 ENDIF 1110 1067 … … 1116 1073 ! 1117 1074 !-- Compute observation height above ground. Note, for trajectory measurements the height 1118 !-- above the surface is actually stored in 'height' 1075 !-- above the surface is actually stored in 'height'. 1119 1076 IF ( vmea(l)%trajectory ) THEN 1120 1077 zar = height … … 1127 1084 !-- on subdomain. This case, setup grid index space sample these quantities. 1128 1085 meas_flag = 0 1129 DO t = 1, vmea(l)%n _tr_st1086 DO t = 1, vmea(l)%nst 1130 1087 ! 1131 1088 !-- First, compute relative x- and y-coordinates with respect to the lower-left origin of … … 1133 1090 !-- is not correct, the virtual sites will be misplaced. Further, in case of an rotated 1134 1091 !-- model domain, the UTM coordinates must also be rotated. 1135 e_utm_tmp(t ,1:dim_ntime) = e_utm(t,1:dim_ntime) - init_model%origin_x1136 n_utm_tmp(t ,1:dim_ntime) = n_utm(t,1:dim_ntime) - init_model%origin_y1137 e_utm(t ,1:dim_ntime) = COS( init_model%rotation_angle * pi / 180.0_wp )&1138 * e_utm_tmp(t ,1:dim_ntime)&1092 e_utm_tmp(t) = e_utm(t) - init_model%origin_x 1093 n_utm_tmp(t) = n_utm(t) - init_model%origin_y 1094 e_utm(t) = COS( init_model%rotation_angle * pi / 180.0_wp ) & 1095 * e_utm_tmp(t) & 1139 1096 - SIN( init_model%rotation_angle * pi / 180.0_wp ) & 1140 * n_utm_tmp(t ,1:dim_ntime)1141 n_utm(t ,1:dim_ntime) = SIN( init_model%rotation_angle * pi / 180.0_wp )&1142 * e_utm_tmp(t ,1:dim_ntime)&1097 * n_utm_tmp(t) 1098 n_utm(t) = SIN( init_model%rotation_angle * pi / 180.0_wp ) & 1099 * e_utm_tmp(t) & 1143 1100 + COS( init_model%rotation_angle * pi / 180.0_wp ) & 1144 * n_utm_tmp(t,1:dim_ntime) 1145 ! 1146 !-- Determine the individual time coordinate length for each station and trajectory. This 1147 !-- is required as several stations and trajectories are merged into one file but they do 1148 !-- not have the same number of points in time, hence, missing values may occur and cannot 1149 !-- be processed further. This is actually a work-around for the specific (UC)2 dataset, 1150 !-- but it won't harm anyway. 1151 vmea(l)%dim_t(t) = 0 1152 DO n = 1, dim_ntime 1153 IF ( e_utm(t,n) /= fill_eutm .AND. n_utm(t,n) /= fill_nutm .AND. & 1154 zar(t,n) /= fill_zar ) vmea(l)%dim_t(t) = n 1155 ENDDO 1101 * n_utm_tmp(t) 1156 1102 ! 1157 1103 !-- Compute grid indices relative to origin and check if these are on the subdomain. Note, … … 1170 1116 ENDIF 1171 1117 1172 DO n = 1, vmea(l)%dim_t(t) 1173 is = INT( ( e_utm(t,n) + 0.5_wp * dx ) * ddx, KIND = iwp ) 1174 js = INT( ( n_utm(t,n) + 0.5_wp * dy ) * ddy, KIND = iwp ) 1175 ! 1176 !-- Is the observation point on subdomain? 1177 on_pe = ( is >= nxl .AND. is <= nxr .AND. js >= nys .AND. js <= nyn ) 1178 ! 1179 !-- Check if observation coordinate is on subdomain. 1180 IF ( on_pe ) THEN 1181 ! 1182 !-- Determine vertical index which corresponds to the observation height. 1183 ksurf = topo_top_ind(js,is,0) 1184 ks = MINLOC( ABS( zu - zw(ksurf) - zar(t,n) ), DIM = 1 ) - 1 1185 ! 1186 !-- Set mask array at the observation coordinates. Also, flag the surrounding 1187 !-- coordinate points, but first check whether the surrounding coordinate points are 1188 !-- on the subdomain. 1189 kl = MERGE( ks-off_z, ksurf, ks-off_z >= nzb .AND. ks-off_z >= ksurf ) 1190 ku = MERGE( ks+off_z, nzt, ks+off_z < nzt+1 ) 1191 1192 DO i = is-off, is+off 1193 DO j = js-off, js+off 1194 DO k = kl, ku 1195 meas_flag(k,j,i) = MERGE( IBSET( meas_flag(k,j,i), 0 ), 0, & 1196 BTEST( wall_flags_total_0(k,j,i), 0 ) ) 1197 ENDDO 1118 is = INT( ( e_utm(t) + 0.5_wp * dx ) * ddx, KIND = iwp ) 1119 js = INT( ( n_utm(t) + 0.5_wp * dy ) * ddy, KIND = iwp ) 1120 ! 1121 !-- Is the observation point on subdomain? 1122 on_pe = ( is >= nxl .AND. is <= nxr .AND. js >= nys .AND. js <= nyn ) 1123 ! 1124 !-- Check if observation coordinate is on subdomain. 1125 IF ( on_pe ) THEN 1126 ! 1127 !-- Determine vertical index which corresponds to the observation height. 1128 ksurf = topo_top_ind(js,is,0) 1129 ks = MINLOC( ABS( zu - zw(ksurf) - zar(t) ), DIM = 1 ) - 1 1130 ! 1131 !-- Set mask array at the observation coordinates. Also, flag the surrounding 1132 !-- coordinate points, but first check whether the surrounding coordinate points are 1133 !-- on the subdomain. 1134 kl = MERGE( ks-off_z, ksurf, ks-off_z >= nzb .AND. ks-off_z >= ksurf ) 1135 ku = MERGE( ks+off_z, nzt, ks+off_z < nzt+1 ) 1136 1137 DO i = is-off, is+off 1138 DO j = js-off, js+off 1139 DO k = kl, ku 1140 meas_flag(k,j,i) = MERGE( IBSET( meas_flag(k,j,i), 0 ), 0, & 1141 BTEST( wall_flags_total_0(k,j,i), 0 ) ) 1198 1142 ENDDO 1199 1143 ENDDO 1200 END IF1201 END DO1144 ENDDO 1145 ENDIF 1202 1146 1203 1147 ENDDO … … 1317 1261 IF ( ALLOCATED( n_utm_tmp ) ) DEALLOCATE( n_utm_tmp ) 1318 1262 IF ( ALLOCATED( n_utm ) ) DEALLOCATE( n_utm ) 1319 IF ( ALLOCATED( zar ) ) DEALLOCATE( vmea(l)%dim_t )1320 1263 IF ( ALLOCATED( zar ) ) DEALLOCATE( zar ) 1321 1264 IF ( ALLOCATED( height ) ) DEALLOCATE( height ) … … 1336 1279 ns_all = 0 1337 1280 #if defined( __parallel ) 1338 CALL MPI_ALLREDUCE( vmea(:)%ns, ns_all(:), vmea_general%nvm, & 1339 MPI_INTEGER, MPI_SUM, comm2d, ierr ) 1281 CALL MPI_ALLREDUCE( vmea(:)%ns, ns_all(:), vmea_general%nvm, MPI_INTEGER, MPI_SUM, comm2d, ierr ) 1340 1282 #else 1341 1283 ns_all(:) = vmea(:)%ns … … 1346 1288 ns_all = 0 1347 1289 #if defined( __parallel ) 1348 CALL MPI_ALLREDUCE( vmea(:)%ns_soil, ns_all(:), vmea_general%nvm, & 1349 MPI_INTEGER, MPI_SUM, comm2d, ierr ) 1290 CALL MPI_ALLREDUCE( vmea(:)%ns_soil, ns_all(:), vmea_general%nvm, MPI_INTEGER, MPI_SUM, comm2d, ierr ) 1350 1291 #else 1351 1292 ns_all(:) = vmea(:)%ns_soil … … 1403 1344 1404 1345 #endif 1405 1346 IF ( debug_output ) CALL debug_message( 'vm_init', 'end' ) 1406 1347 END SUBROUTINE vm_init 1407 1348 … … 1999 1940 REAL(wp), DIMENSION(:), ALLOCATABLE :: dum_lon !< transformed geographical coordinate (longitude) 2000 1941 REAL(wp), DIMENSION(:), ALLOCATABLE :: oro_rel !< relative altitude of model surface 2001 REAL( wp), DIMENSION(:), POINTER :: output_values_1d_pointer !< pointer for 1d output array2002 REAL( wp), DIMENSION(:), ALLOCATABLE, TARGET :: output_values_1d_target !< target for 1d output array2003 REAL( wp), DIMENSION(:,:), POINTER :: output_values_2d_pointer !< pointer for 2d output array2004 REAL( wp), DIMENSION(:,:), ALLOCATABLE, TARGET :: output_values_2d_target !< target for 2d output array1942 REAL(sp), DIMENSION(:), POINTER :: output_values_1d_pointer !< pointer for 1d output array 1943 REAL(sp), DIMENSION(:), ALLOCATABLE, TARGET :: output_values_1d_target !< target for 1d output array 1944 REAL(sp), DIMENSION(:,:), POINTER :: output_values_2d_pointer !< pointer for 2d output array 1945 REAL(sp), DIMENSION(:,:), ALLOCATABLE, TARGET :: output_values_2d_target !< target for 2d output array 2005 1946 2006 1947 CALL cpu_log( log_point_s(26), 'VM output', 'start' ) … … 2027 1968 2028 1969 return_value = dom_write_var( vmea(l)%nc_filename, 'E_UTM', & 2029 values_real wp_1d = output_values_1d_pointer, &1970 values_real32_1d = output_values_1d_pointer, & 2030 1971 bounds_start = (/vmea(l)%start_coord_a/), & 2031 1972 bounds_end = (/vmea(l)%end_coord_a /) ) … … 2040 1981 output_values_1d_pointer => output_values_1d_target 2041 1982 return_value = dom_write_var( vmea(l)%nc_filename, 'N_UTM', & 2042 values_real wp_1d = output_values_1d_pointer, &1983 values_real32_1d = output_values_1d_pointer, & 2043 1984 bounds_start = (/vmea(l)%start_coord_a/), & 2044 1985 bounds_end = (/vmea(l)%end_coord_a /) ) … … 2066 2007 output_values_1d_pointer => output_values_1d_target 2067 2008 return_value = dom_write_var( vmea(l)%nc_filename, 'lat', & 2068 values_real wp_1d = output_values_1d_pointer, &2009 values_real32_1d = output_values_1d_pointer, & 2069 2010 bounds_start = (/vmea(l)%start_coord_a/), & 2070 2011 bounds_end = (/vmea(l)%end_coord_a /) ) … … 2073 2014 output_values_1d_pointer => output_values_1d_target 2074 2015 return_value = dom_write_var( vmea(l)%nc_filename, 'lon', & 2075 values_real wp_1d = output_values_1d_pointer, &2016 values_real32_1d = output_values_1d_pointer, & 2076 2017 bounds_start = (/vmea(l)%start_coord_a/), & 2077 2018 bounds_end = (/vmea(l)%end_coord_a /) ) … … 2089 2030 output_values_1d_pointer => output_values_1d_target 2090 2031 return_value = dom_write_var( vmea(l)%nc_filename, 'z', & 2091 values_real wp_1d = output_values_1d_pointer, &2032 values_real32_1d = output_values_1d_pointer, & 2092 2033 bounds_start = (/vmea(l)%start_coord_a/), & 2093 2034 bounds_end = (/vmea(l)%end_coord_a /) ) … … 2098 2039 output_values_1d_pointer => output_values_1d_target 2099 2040 return_value = dom_write_var( vmea(l)%nc_filename, 'station_h', & 2100 values_real wp_1d = output_values_1d_pointer, &2041 values_real32_1d = output_values_1d_pointer, & 2101 2042 bounds_start = (/vmea(l)%start_coord_a/), & 2102 2043 bounds_end = (/vmea(l)%end_coord_a /) ) … … 2141 2082 output_values_1d_pointer => output_values_1d_target 2142 2083 return_value = dom_write_var( vmea(l)%nc_filename, 'E_UTM_soil', & 2143 values_real wp_1d = output_values_1d_pointer, &2084 values_real32_1d = output_values_1d_pointer, & 2144 2085 bounds_start = (/vmea(l)%start_coord_s/), & 2145 2086 bounds_end = (/vmea(l)%end_coord_s /) ) … … 2154 2095 output_values_1d_pointer => output_values_1d_target 2155 2096 return_value = dom_write_var( vmea(l)%nc_filename, 'N_UTM_soil', & 2156 values_real wp_1d = output_values_1d_pointer, &2097 values_real32_1d = output_values_1d_pointer, & 2157 2098 bounds_start = (/vmea(l)%start_coord_s/), & 2158 2099 bounds_end = (/vmea(l)%end_coord_s /) ) … … 2180 2121 output_values_1d_pointer => output_values_1d_target 2181 2122 return_value = dom_write_var( vmea(l)%nc_filename, 'lat_soil', & 2182 values_real wp_1d = output_values_1d_pointer, &2123 values_real32_1d = output_values_1d_pointer, & 2183 2124 bounds_start = (/vmea(l)%start_coord_s/), & 2184 2125 bounds_end = (/vmea(l)%end_coord_s /) ) … … 2187 2128 output_values_1d_pointer => output_values_1d_target 2188 2129 return_value = dom_write_var( vmea(l)%nc_filename, 'lon_soil', & 2189 values_real wp_1d = output_values_1d_pointer, &2130 values_real32_1d = output_values_1d_pointer, & 2190 2131 bounds_start = (/vmea(l)%start_coord_s/), & 2191 2132 bounds_end = (/vmea(l)%end_coord_s /) ) … … 2203 2144 output_values_1d_pointer => output_values_1d_target 2204 2145 return_value = dom_write_var( vmea(l)%nc_filename, 'z_soil', & 2205 values_real wp_1d = output_values_1d_pointer, &2146 values_real32_1d = output_values_1d_pointer, & 2206 2147 bounds_start = (/vmea(l)%start_coord_s/), & 2207 2148 bounds_end = (/vmea(l)%end_coord_s /) ) … … 2212 2153 output_values_1d_pointer => output_values_1d_target 2213 2154 return_value = dom_write_var( vmea(l)%nc_filename, 'station_h_soil', & 2214 values_real wp_1d = output_values_1d_pointer, &2155 values_real32_1d = output_values_1d_pointer, & 2215 2156 bounds_start = (/vmea(l)%start_coord_s/), & 2216 2157 bounds_end = (/vmea(l)%end_coord_s /) ) … … 2270 2211 2271 2212 return_value = dom_write_var( vmea(l)%nc_filename, variable_name, & 2272 values_real wp_2d = output_values_2d_pointer, &2213 values_real32_2d = output_values_2d_pointer, & 2273 2214 bounds_start = (/vmea(l)%start_coord_s, t_ind/), & 2274 2215 bounds_end = (/vmea(l)%end_coord_s, t_ind /) ) … … 2278 2219 output_values_2d_pointer => output_values_2d_target 2279 2220 return_value = dom_write_var( vmea(l)%nc_filename, variable_name, & 2280 values_real wp_2d = output_values_2d_pointer, &2221 values_real32_2d = output_values_2d_pointer, & 2281 2222 bounds_start = (/vmea(l)%start_coord_s, t_ind/), & 2282 2223 bounds_end = (/vmea(l)%end_coord_s, t_ind /) ) … … 2291 2232 2292 2233 return_value = dom_write_var( vmea(l)%nc_filename, variable_name, & 2293 values_real wp_2d = output_values_2d_pointer, &2234 values_real32_2d = output_values_2d_pointer, & 2294 2235 bounds_start = (/vmea(l)%start_coord_a, t_ind/), & 2295 2236 bounds_end = (/vmea(l)%end_coord_a, t_ind/) ) … … 2300 2241 output_values_2d_pointer => output_values_2d_target 2301 2242 return_value = dom_write_var( vmea(l)%nc_filename, variable_name, & 2302 values_real wp_2d = output_values_2d_pointer, &2243 values_real32_2d = output_values_2d_pointer, & 2303 2244 bounds_start = (/ vmea(l)%start_coord_a, t_ind /), & 2304 2245 bounds_end = (/ vmea(l)%end_coord_a, t_ind /) )
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