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How can I extract specific weather data from a 3D grid?
15 ビュー (過去 30 日間)
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I have two sets of data - one is measured data, and the other is obtained from The THREDDS Data Server (files are too big to attach, so I'll try to explain as much as I can).
The measured data include time (measured time in datatime format), latitude coordinates and longitude coordinates, and the parameter I'm interested in. All are column vectors. From THREDDS data, I have time (in datetime format), latitude coordinates and longitude coordinates, and the parameter as follows.
Name Size Bytes Class Attributes
dates 24x1 192 datetime
ff 2379x1995x24 455626080 single
lat 2379x1995 18984420 single
lon 2379x1995 18984420 single
What I want to do is extract the data from this THREADDS data for the given (or measured) time, lat, lot points. How can I do this?
採用された回答
Star Strider
2025 年 2 月 10 日 16:56
Without even representative data, I’m not certain even how to simulate this.
I would use the scatteredInterpolant function first with the THREADDS data (time, latitude, longitude and parameter) and then use the time, latitude, and longitude from the ‘measured’ data to interpolate the desired paramter at the ‘measured’ times and locations. (If I got this backwards, change my approach approproately.)
I don’t remember using scatteredInterpolant with datetime arrays, and the scatteredInterpolant documentation states that all the data have to be double, so first convert those to datenum or other numerical date representations that scattteredInterpolant can work with. You can change them back later.
.
6 件のコメント
Jake
2025 年 2 月 10 日 17:47
I understand. I spent some time separating a smaller sample from my data sets. I've attached it herewith. The actual data samples are way too big (~1.5GB), so I hope I didn't chop it the wrong way.
I'm trying what you suggested right away, but I wanted to share this data sample too :)
load("repData.mat"); whos
Star Strider
2025 年 2 月 10 日 18:30
I’m not certain how to plot it, however the interpolation seems to work. You will need to check it to be sure it does what you want.
NOTE — using repmat is necessary to get all the arrays to have the same dimensions before sending them to scatteredInterpolant. Also, the single arrays need to be cast to double first.
Try something like this —
LD = load('repData.mat')
LD = struct with fields:
dataSample_01: [1x1 struct]
dataSample_02: [1x1 struct]
DS1 = LD.dataSample_01
DS1 = struct with fields:
measTime: [100x1 datetime]
measLat: [100x1 double]
measLon: [100x1 double]
dates1n = datenum(DS1.measTime);
lat1 = DS1.measLat;
lon1 = DS1.measLon;
DS2 = LD.dataSample_02
DS2 = struct with fields:
ff: [48x40x24 single]
lat: [48x40 single]
lon: [48x40 single]
dates: [24x1 datetime]
ff = double(DS2.ff);
lat2 = double(repmat(DS2.lat, 1, 1, 24));
lon2 = double(repmat(DS2.lon, 1, 1, 24));
dates2n = repmat(datenum(DS2.dates), size(lat2,1), size(lat2,2), 1);
DS2fcn = scatteredInterpolant(lat2(:), lon2(:), dates2n(:), ff(:))
Warning: Duplicate data points have been detected and removed - corresponding values have been averaged.
DS2fcn =
scatteredInterpolant with properties:
Points: [1920x3 double]
Values: [1920x1 double]
Method: 'linear'
ExtrapolationMethod: 'linear'
ff_interp = DS2fcn(lat1(:), lon1(:), dates1n(:));
disp(ff_interp)
1.0e+06 *
-5.6908
-5.6972
-5.7037
-5.7103
-5.7168
-5.7234
-5.7299
-5.7364
-5.7428
-5.7492
-5.7555
-5.7619
-5.7683
-5.7747
-5.7813
-5.7879
-5.7944
-5.8010
-5.8074
-5.8138
-5.8202
-5.8265
-5.8329
-5.8393
-5.8458
-5.8523
-5.8589
-5.8654
-5.8719
-5.8784
-5.8849
-5.8913
-5.8976
-5.9040
-5.9104
-5.9168
-5.9233
-5.9298
-5.9363
-5.9428
-5.9493
-5.9558
-5.9623
-5.9687
-5.9752
-5.9816
-5.9879
-5.9944
-6.0008
-6.0073
-6.0138
-6.0203
-6.0268
-6.0333
-6.0397
-6.0462
-6.0526
-6.0590
-6.0654
-6.0718
-6.0782
-6.0847
-6.0912
-6.0977
-6.1042
-6.1107
-6.1171
-6.1235
-6.1299
-6.1363
-6.1427
-6.1492
-6.1556
-6.1621
-6.1686
-6.1751
-6.1816
-6.1881
-6.1946
-6.2010
-6.2074
-6.2138
-6.2202
-6.2266
-6.2331
-6.2396
-6.2461
-6.2526
-6.2591
-6.2656
-6.2720
-6.2785
-6.2849
-6.2913
-6.2977
-6.3042
-6.3106
-6.3171
-6.3236
-6.3301
.
Jake
2025 年 2 月 11 日 7:54
Hi @Star Strider, this is a good start. But I'm not sure if it gives the right results - I may have explained the goal poorly, so I will try explaining better. I have measured data for a given area grid (lat, lon) at a given time interval. I also have weather data for this grid for a time interval. I'm trying to see if the measured data matches the THREADDS data.
Say, the parameter I'm interested in is ff. In the measured values, we can see that the maximum is < 25. But the interpolated ff gives a value much higher (in the order of 1.0e+06). Maybe the dimension here is wrong, due to datenum?
Star Strider
2025 年 2 月 11 日 12:25
The results being on the order of
turns out to be an artefact of the data, not the date representation. The scatteredInterpolant function does not accept datetime values as arguments, only double values, so there has to be some provision for converting the datetime values to some sort of double representation.

Since they all occur on the same day,. one option is to convert them to duration arrays, and speecifically with seconds, although datenum should not influence those, since they all should have the same relative ranges. Using datenum remains an option, and should not affect the results since they all occur on the same day.
That aside, the interpolation in my original code is correct. I didn’t look at the data previously, however it seems that ‘ff’ has a number (23064 in this sample) of missing values, those being denoted by -9999999, and they are throwing off the calculations. Replacing them with NaN produces NaN for the interpolated values (as would be expected). Replacing them with 0 is marginally better (the results make sense, at least in terms of magnitude), however I must leave it to you to determine if the interpolated results with that change are appropriate for the matchinng times and locations.
So doing something appropriate with the missing values turnns out to be the important step in getting the interpolation to work correctly.
Try this —
LD = load('repData.mat')
LD = struct with fields:
dataSample_01: [1x1 struct]
dataSample_02: [1x1 struct]
DS1 = LD.dataSample_01
DS1 = struct with fields:
measTime: [100x1 datetime]
measLat: [100x1 double]
measLon: [100x1 double]
[datestart1,dateeend1] = bounds(DS1.measTime)
datestart1 = datetime
2022-11-10T20:30:00.0000000Z
dateeend1 = datetime
2022-11-10T20:31:39.0000000Z
disp(DS1.measTime)
2022-11-10T20:30:00.0000000Z
2022-11-10T20:30:01.0000000Z
2022-11-10T20:30:02.0000000Z
2022-11-10T20:30:03.0000000Z
2022-11-10T20:30:04.0000000Z
2022-11-10T20:30:05.0000000Z
2022-11-10T20:30:06.0000000Z
2022-11-10T20:30:07.0000000Z
2022-11-10T20:30:08.0000000Z
2022-11-10T20:30:09.0000000Z
2022-11-10T20:30:10.0000000Z
2022-11-10T20:30:11.0000000Z
2022-11-10T20:30:12.0000000Z
2022-11-10T20:30:13.0000000Z
2022-11-10T20:30:14.0000000Z
2022-11-10T20:30:15.0000000Z
2022-11-10T20:30:16.0000000Z
2022-11-10T20:30:17.0000000Z
2022-11-10T20:30:18.0000000Z
2022-11-10T20:30:19.0000000Z
2022-11-10T20:30:20.0000000Z
2022-11-10T20:30:21.0000000Z
2022-11-10T20:30:22.0000000Z
2022-11-10T20:30:23.0000000Z
2022-11-10T20:30:24.0000000Z
2022-11-10T20:30:25.0000000Z
2022-11-10T20:30:26.0000000Z
2022-11-10T20:30:27.0000000Z
2022-11-10T20:30:28.0000000Z
2022-11-10T20:30:29.0000000Z
2022-11-10T20:30:30.0000000Z
2022-11-10T20:30:31.0000000Z
2022-11-10T20:30:32.0000000Z
2022-11-10T20:30:33.0000000Z
2022-11-10T20:30:34.0000000Z
2022-11-10T20:30:35.0000000Z
2022-11-10T20:30:36.0000000Z
2022-11-10T20:30:37.0000000Z
2022-11-10T20:30:38.0000000Z
2022-11-10T20:30:39.0000000Z
2022-11-10T20:30:40.0000000Z
2022-11-10T20:30:41.0000000Z
2022-11-10T20:30:42.0000000Z
2022-11-10T20:30:43.0000000Z
2022-11-10T20:30:44.0000000Z
2022-11-10T20:30:45.0000000Z
2022-11-10T20:30:46.0000000Z
2022-11-10T20:30:47.0000000Z
2022-11-10T20:30:48.0000000Z
2022-11-10T20:30:49.0000000Z
2022-11-10T20:30:50.0000000Z
2022-11-10T20:30:51.0000000Z
2022-11-10T20:30:52.0000000Z
2022-11-10T20:30:53.0000000Z
2022-11-10T20:30:54.0000000Z
2022-11-10T20:30:55.0000000Z
2022-11-10T20:30:56.0000000Z
2022-11-10T20:30:57.0000000Z
2022-11-10T20:30:58.0000000Z
2022-11-10T20:30:59.0000000Z
2022-11-10T20:31:00.0000000Z
2022-11-10T20:31:01.0000000Z
2022-11-10T20:31:02.0000000Z
2022-11-10T20:31:03.0000000Z
2022-11-10T20:31:04.0000000Z
2022-11-10T20:31:05.0000000Z
2022-11-10T20:31:06.0000000Z
2022-11-10T20:31:07.0000000Z
2022-11-10T20:31:08.0000000Z
2022-11-10T20:31:09.0000000Z
2022-11-10T20:31:10.0000000Z
2022-11-10T20:31:11.0000000Z
2022-11-10T20:31:12.0000000Z
2022-11-10T20:31:13.0000000Z
2022-11-10T20:31:14.0000000Z
2022-11-10T20:31:15.0000000Z
2022-11-10T20:31:16.0000000Z
2022-11-10T20:31:17.0000000Z
2022-11-10T20:31:18.0000000Z
2022-11-10T20:31:19.0000000Z
2022-11-10T20:31:20.0000000Z
2022-11-10T20:31:21.0000000Z
2022-11-10T20:31:22.0000000Z
2022-11-10T20:31:23.0000000Z
2022-11-10T20:31:24.0000000Z
2022-11-10T20:31:25.0000000Z
2022-11-10T20:31:26.0000000Z
2022-11-10T20:31:27.0000000Z
2022-11-10T20:31:28.0000000Z
2022-11-10T20:31:29.0000000Z
2022-11-10T20:31:30.0000000Z
2022-11-10T20:31:31.0000000Z
2022-11-10T20:31:32.0000000Z
2022-11-10T20:31:33.0000000Z
2022-11-10T20:31:34.0000000Z
2022-11-10T20:31:35.0000000Z
2022-11-10T20:31:36.0000000Z
2022-11-10T20:31:37.0000000Z
2022-11-10T20:31:38.0000000Z
2022-11-10T20:31:39.0000000Z
time1n = seconds(DS1.measTime - DS1.measTime(1));
disp(time1n)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
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18
19
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lat1 = DS1.measLat;
lon1 = DS1.measLon;
DS2 = LD.dataSample_02
DS2 = struct with fields:
ff: [48x40x24 single]
lat: [48x40 single]
lon: [48x40 single]
dates: [24x1 datetime]
[datestart2,dateeend2] = bounds(DS2.dates)
datestart2 = datetime
10-Nov-2022
dateeend2 = datetime
10-Nov-2022 23:00:00
disp(DS2.dates)
10-Nov-2022 00:00:00
10-Nov-2022 01:00:00
10-Nov-2022 02:00:00
10-Nov-2022 03:00:00
10-Nov-2022 04:00:00
10-Nov-2022 05:00:00
10-Nov-2022 06:00:00
10-Nov-2022 07:00:00
10-Nov-2022 08:00:00
10-Nov-2022 09:00:00
10-Nov-2022 10:00:00
10-Nov-2022 11:00:00
10-Nov-2022 12:00:00
10-Nov-2022 13:00:00
10-Nov-2022 14:00:00
10-Nov-2022 15:00:00
10-Nov-2022 16:00:00
10-Nov-2022 17:00:00
10-Nov-2022 18:00:00
10-Nov-2022 19:00:00
10-Nov-2022 20:00:00
10-Nov-2022 21:00:00
10-Nov-2022 22:00:00
10-Nov-2022 23:00:00
time2n = seconds(DS2.dates - DS2.dates(1));
disp(time2n)
0
3600
7200
10800
14400
18000
21600
25200
28800
32400
36000
39600
43200
46800
50400
54000
57600
61200
64800
68400
72000
75600
79200
82800
ff = double(DS2.ff);
lat2 = double(repmat(DS2.lat, 1, 1, 24));
lon2 = double(repmat(DS2.lon, 1, 1, 24));
dates2n = repmat(time2n, size(lat2,1), size(lat2,2), 1);
format longG % Optional, Displays Data To Full Precision
[ffmin1,ffmax1] = bounds(ff,'all')
ffmin1 =
-9999999
ffmax1 =
23.2523384094238
disp(ff)
(:,:,1) =
Columns 1 through 6
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 5.49097442626953 4.10185194015503
-9999999 -9999999 -9999999 -9999999 -9999999 10.2733430862427
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 12.0239324569702 11.0271291732788 12.6883811950684
14.6009979248047 15.040488243103 13.9954643249512 13.4685401916504 14.7193717956543 13.7912921905518
18.1889476776123 17.8267974853516 16.6448593139648 15.7711582183838 13.6828966140747 10.0988235473633
18.1524047851562 17.3554420471191 16.2057514190674 14.7649021148682 13.360333442688 13.1142883300781
17.4133243560791 16.9827480316162 16.0072574615479 14.1553087234497 13.6371927261353 14.1954650878906
18.8466129302979 17.9846515655518 16.4167747497559 13.2546415328979 9.27805328369141 -9999999
19.0505199432373 16.4864730834961 15.3494882583618 15.302303314209 15.3607587814331 -9999999
8.85022640228271 7.32934904098511 13.3964500427246 14.7070245742798 14.3706712722778 16.7121105194092
8.04390335083008 6.75274896621704 7.08383893966675 11.0589084625244 13.1564111709595 13.0542020797729
2 6.10072040557861 3.04391956329346 6.75179767608643 9.55458641052246 8.64268112182617
9.06277465820312 3.51199293136597 2 2 4.86570692062378 4.38352870941162
12.4875411987305 7.45359516143799 4.17208099365234 6.25336980819702 4.89185428619385 3.82021760940552
12.1183767318726 10.4626808166504 7.56360673904419 11.3432149887085 7.24603652954102 6.59108543395996
12.0366039276123 11.8663969039917 9.05874347686768 11.8592491149902 15.3204507827759 13.7393522262573
12.0827608108521 12.8820056915283 11.8651819229126 11.1430797576904 12.6135139465332 11.8443412780762
16.6719913482666 13.5129499435425 14.3504180908203 12.7478637695312 9.08226013183594 8.27779769897461
16.850284576416 17.4930877685547 16.612024307251 17.3330059051514 16.2979679107666 10.4561138153076
16.1292114257812 16.9691886901855 16.3953914642334 17.4923305511475 18.8700218200684 18.3255176544189
14.8970823287964 15.7485389709473 16.428150177002 16.0918369293213 18.6950340270996 18.7824726104736
13.3919038772583 13.9135580062866 -9999999 -9999999 -9999999 14.3393440246582
10.6612205505371 11.670506477356 12.1661787033081 -9999999 13.6562423706055 -9999999
7.9140625 9.86591720581055 10.5791292190552 -9999999 -9999999 -9999999
6.15274381637573 9.57742786407471 9.97864627838135 -9999999 -9999999 -9999999
4.07768249511719 4.58499908447266 8.09462261199951 10.5065927505493 -9999999 10.1286106109619
2.53344893455505 -9999999 -9999999 -9999999 -9999999 4.64694786071777
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
Columns 7 through 12
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 5.81832504272461 12.8119888305664 9.78480529785156 8.96708297729492
10.9697780609131 7.77531719207764 10.7183237075806 10.855749130249 8.26909828186035 7.42667293548584
9.39523601531982 7.68783855438232 11.0579795837402 8.32451343536377 6.91351890563965 7.29436874389648
4.08024644851685 7.53002643585205 8.37025737762451 6.40215587615967 6.28318452835083 6.86167240142822
-9999999 -9999999 6.75266027450562 5.59868717193604 7.27081871032715 6.28839921951294
-9999999 -9999999 -9999999 -9999999 4.77573108673096 6.35665893554688
2 -9999999 -9999999 7.46069478988647 6.45794057846069 5.41803073883057
7.44273662567139 8.67431545257568 6.03516721725464 -9999999 -9999999 5.87108039855957
-9999999 8.35557556152344 10.1756114959717 -9999999 -9999999 -9999999
10.8497886657715 8.27526950836182 8.15560340881348 13.2804880142212 5.80753183364868 -9999999
11.3393144607544 11.1448431015015 6.98972177505493 9.00418376922607 -9999999 6.38206338882446
9.68697166442871 11.3550310134888 11.6176147460938 12.1776895523071 11.655385017395 9.69606971740723
13.2803173065186 13.9170045852661 15.4757328033447 12.0398397445679 13.8806133270264 11.3511409759521
-9999999 -9999999 -9999999 -9999999 -9999999 2
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
17.4248237609863 13.5168952941895 -9999999 -9999999 -9999999 -9999999
13.8420495986938 17.7624988555908 15.37477684021 -9999999 -9999999 -9999999
12.7151250839233 16.0114765167236 19.0553817749023 12.2921123504639 -9999999 -9999999
12.3756141662598 13.8661851882935 17.8609962463379 19.0278797149658 14.4245977401733 -9999999
9.29625797271729 10.4600057601929 10.0525550842285 20.3644466400146 20.8720512390137 20.1030426025391
2 11.0917463302612 5.34957885742188 6.73184442520142 13.9319839477539 17.5093193054199
6.92937421798706 13.6170263290405 -9999999 -9999999 11.3248510360718 10.4091548919678
11.7649173736572 8.52396392822266 12.5298900604248 -9999999 8.44614028930664 10.405740737915
10.4001054763794 7.26922845840454 6.33134794235229 8.91607666015625 7.91469717025757 10.2079725265503
8.23428440093994 6.77507877349854 8.49717712402344 5.01592206954956 8.12884712219238 8.26519393920898
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Columns 37 through 40
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(:,:,2) =
Columns 1 through 6
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Columns 13 through 18
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Columns 19 through 24
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Columns 25 through 30
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Columns 31 through 36
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2 2.98638010025024 5.24778127670288 6.12347459793091 7.01166963577271 8.2203369140625
3.63103723526001 2 2 5.08004808425903 6.82609415054321 8.33810138702393
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4.22236204147339 4.40734815597534 3.30116176605225 2.69338011741638 5.04357576370239 5.87648677825928
3.77929759025574 3.29751300811768 2.86433815956116 2 4.25711441040039 5.31156873703003
3.25812768936157 4.017333984375 3.65276384353638 3.15697002410889 3.07542991638184 -9999999
2 5.35522603988647 2.13997864723206 2 4.84615707397461 -9999999
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Columns 37 through 40
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7.14300394058228 8.73792266845703 10.3068494796753 12.5247163772583
7.68981552124023 7.22637319564819 10.3753280639648 12.4621324539185
6.21790599822998 5.7204475402832 8.18760395050049 10.4111289978027
6.6418342590332 5.43643283843994 7.10835790634155 8.39835834503174
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6.61197566986084 7.02138566970825 7.97982883453369 5.5793023109436
5.77715921401978 4.37394428253174 7.18990707397461 6.3227744102478
4.54339361190796 4.50572109222412 5.57078552246094 7.36010408401489
5.49521684646606 2.42243695259094 5.47277641296387 4.79975032806396
6.24387121200562 -9999999 -9999999 -9999999
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2 -9999999 -9999999 -9999999
2 -9999999 -9999999 -9999999
3.40326952934265 -9999999 -9999999 -9999999
5.33806276321411 -9999999 -9999999 -9999999
6.97660160064697 -9999999 -9999999 -9999999
8.02286052703857 -9999999 -9999999 -9999999
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-9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999
(:,:,3) =
Columns 1 through 6
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-9999999 -9999999 -9999999 -9999999 5.13754367828369 3.64833736419678
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-9999999 -9999999 -9999999 11.2639379501343 9.77473831176758 11.8204975128174
12.7173709869385 13.6319808959961 12.9813289642334 12.4576787948608 13.790018081665 13.2231035232544
16.2801952362061 15.7157897949219 14.9923982620239 14.4900131225586 13.7140817642212 10.3553314208984
17.9294624328613 17.3811283111572 16.1628170013428 13.9939489364624 11.9938049316406 12.0571565628052
17.4024868011475 16.9661903381348 15.5198020935059 14.3647994995117 13.5410270690918 13.9779024124146
18.2043151855469 17.153938293457 15.3206663131714 12.5512418746948 9.02318000793457 -9999999
18.1760139465332 14.7595338821411 15.5820226669312 15.1399192810059 13.3246717453003 -9999999
9.08199214935303 10.1220397949219 14.0152168273926 14.0130071640015 14.9849615097046 16.2499961853027
8.00566387176514 6.94630146026611 9.01914310455322 12.6835489273071 12.9837799072266 10.1680202484131
3.16804194450378 6.40235137939453 3.00520968437195 7.03916072845459 8.5525598526001 11.1598949432373
7.78936338424683 3.31905579566956 2 2 3.51631784439087 3.22952556610107
13.1334838867188 7.02984237670898 4.37837553024292 6.17976427078247 4.15615224838257 3.93521785736084
13.0454521179199 11.5018014907837 8.27502822875977 10.0509614944458 6.9206600189209 5.91892623901367
12.7873086929321 11.9615297317505 9.81848907470703 11.3699207305908 15.7458467483521 11.6763801574707
13.2124710083008 11.520489692688 12.3008661270142 11.5530872344971 13.3240623474121 13.259072303772
11.7104616165161 9.70735359191895 12.6873931884766 11.3096981048584 8.96870231628418 9.99731636047363
16.8504981994629 16.9022121429443 16.1565570831299 14.407904624939 11.9619112014771 10.4764642715454
16.3930397033691 16.8603515625 16.97580909729 18.4708347320557 18.037088394165 19.3560199737549
14.6641874313354 15.5431594848633 17.177173614502 16.6036567687988 18.3317852020264 18.1370944976807
13.3044996261597 14.1539125442505 -9999999 -9999999 -9999999 13.81471824646
11.6942501068115 12.066969871521 12.1167154312134 -9999999 13.5086278915405 -9999999
8.45200347900391 9.6799430847168 10.284538269043 -9999999 -9999999 -9999999
6.37220811843872 8.3126163482666 10.2600231170654 -9999999 -9999999 -9999999
3.46204233169556 4.30958843231201 8.71926689147949 10.313232421875 -9999999 10.7352285385132
2 -9999999 -9999999 -9999999 -9999999 4.20033836364746
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
Columns 7 through 12
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-9999999 -9999999 6.08503389358521 12.0968723297119 9.69642639160156 8.80388832092285
10.4354724884033 4.51473760604858 10.8896684646606 10.0030317306519 7.9230260848999 7.50851154327393
7.62465286254883 7.5511269569397 10.895486831665 7.60040283203125 7.01044321060181 7.27054071426392
5.95288991928101 8.4223747253418 7.78627824783325 6.18099927902222 6.25393915176392 6.93318128585815
-9999999 -9999999 6.9999418258667 6.0028715133667 6.76134061813354 6.04385423660278
-9999999 -9999999 -9999999 -9999999 4.81152391433716 6.63426065444946
2 -9999999 -9999999 6.51652193069458 6.2741756439209 5.45127725601196
5.96982383728027 8.0561056137085 4.30300378799438 -9999999 -9999999 6.11839008331299
-9999999 8.50288009643555 9.38227558135986 -9999999 -9999999 -9999999
9.70293521881104 7.74757194519043 9.12732028961182 13.0050592422485 5.17002010345459 -9999999
10.8414011001587 11.021879196167 6.09590673446655 9.83760166168213 -9999999 6.0848650932312
9.49281692504883 11.2256860733032 11.8377799987793 12.8671360015869 11.4895553588867 9.22302341461182
11.9295721054077 13.5874853134155 15.6843023300171 10.1930170059204 13.0089340209961 10.2199420928955
-9999999 -9999999 -9999999 -9999999 -9999999 2
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17.3137950897217 14.0551595687866 -9999999 -9999999 -9999999 -9999999
15.3437614440918 16.9645805358887 15.5033578872681 -9999999 -9999999 -9999999
14.0211620330811 16.44091796875 19.9376125335693 12.177396774292 -9999999 -9999999
12.2810258865356 12.7059812545776 17.1237297058105 18.4446086883545 16.0345497131348 -9999999
10.6010417938232 9.94300365447998 8.59515762329102 20.5516815185547 20.4501609802246 19.745418548584
3.68146800994873 11.7109470367432 5.6193642616272 12.5282306671143 13.6369304656982 16.3978672027588
2.71259665489197 12.3281879425049 -9999999 -9999999 10.9986057281494 11.4840402603149
11.8983449935913 3.10439419746399 10.9800233840942 -9999999 8.43977069854736 11.1522283554077
10.6249980926514 5.28164863586426 5.97791004180908 7.983238697052 8.68552589416504 9.48833465576172
8.90365028381348 11.071551322937 8.45669269561768 5.19245147705078 8.33822822570801 9.23954677581787
18.1640605926514 12.7640361785889 7.47415018081665 11.597996711731 10.253134727478 8.65752220153809
17.9227714538574 16.9318027496338 11.4005060195923 10.2475748062134 11.0032835006714 8.09084415435791
-9999999 -9999999 14.3858261108398 12.8694934844971 10.3759689331055 10.0889511108398
11.2184133529663 14.3938636779785 13.3125600814819 10.282753944397 10.7024183273315 5.42421007156372
11.8829689025879 13.0821161270142 11.5112209320068 9.37981128692627 -9999999 -9999999
10.7920951843262 11.4810371398926 10.5760822296143 -9999999 -9999999 -9999999
11.2175722122192 10.2631015777588 9.21274757385254 8.07711601257324 9.29081249237061 -9999999
7.81649541854858 9.09658145904541 -9999999 9.01287364959717 -9999999 -9999999
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-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
Columns 13 through 18
-9999999 -9999999 -9999999 -9999999 -9999999 -9999999
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7.6069278717041 -9999999 -9999999 -9999999 -9999999 -9999999
6.86252021789551 -9999999 -9999999 -9999999 -9999999 -9999999
5.91737222671509 -9999999 -9999999 -9999999 -9999999 2
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7.52373647689819 -9999999 7.55381965637207 4.55531597137451 -9999999 3.56437349319458
6.66961145401001 -9999999 -9999999 6.33496952056885 6.96727323532104 -9999999
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3.67514276504517 -9999999 -9999999 -9999999 -9999999 6.43960428237915
-9999999 -9999999 -9999999 2.4306948184967 8.04032802581787 -9999999
8.88760852813721 13.6870603561401 12.4252099990845 11.5359573364258 11.394998550415 6.84907960891724
9.31181716918945 9.54254627227783 6.00718402862549 7.2005763053894 5.19509124755859 10.5125541687012
7.42595338821411 4.85204648971558 5.62501192092896 5.34471368789673 5.54158544540405 3.24889087677002
2 -9999999 -9999999 -9999999 -9999999 -9999999
-9999...
ff_size = size(ff)
ff_size = 1×3
48 40 24
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
ff_missing = numel(ff(ff==ffmin1))
ff_missing =
23064
% ff(ff==ffmin1) = NaN; % Replace Miissing Data With ‘NaN’
ff(ff==ffmin1) = 0; % Replace Miissing Data With ‘0’
[ffmin2,ffmax2] = bounds(ff,'all')
ffmin2 =
0
ffmax2 =
23.2523384094238
ff_size = size(ff)
ff_size = 1×3
48 40 24
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
DS2fcn = scatteredInterpolant(lat2(:), lon2(:), dates2n(:), ff(:))
Warning: Duplicate data points have been detected and removed - corresponding values have been averaged.
DS2fcn =
scatteredInterpolant with properties:
Points: [1920x3 double]
Values: [1920x1 double]
Method: 'linear'
ExtrapolationMethod: 'linear'
ff_interp = DS2fcn(lat1(:), lon1(:), time1n(:));
disp(ff_interp)
5.54006666631625
5.54702016651343
5.55393367024466
5.56081244061639
5.56767427743635
5.57453044249541
5.58139819626902
5.58830376447568
5.59525072665555
5.60223180921315
5.60923303707364
5.61622912442435
5.62319409073957
5.63011223651353
5.63698607070271
5.64383618813011
5.65068589690296
5.65755451693292
5.66446609190566
5.67141799026803
5.67840429506652
5.685402646181
5.69238846059371
5.69934866227014
5.70627395904324
5.71316242215477
5.72003115669957
5.72689488184408
5.73376876512973
5.74067009128579
5.74760164001799
5.75456945029542
5.76155672641392
5.76854293423006
5.77550667458139
5.78244628143462
5.78936155046264
5.79625618380211
5.80314642029187
5.81003611085555
5.81693433032892
5.82384745818945
5.83077797720321
5.83772313322099
5.84463371934104
5.85159758228877
5.85855889541429
5.86550232700075
5.87243206957087
5.87934931630446
5.88625572892876
5.89315375931999
5.90005077901981
5.90695614289903
5.91387692022602
5.92082137344921
5.92778091319644
5.93474575691537
5.94170739696463
5.94865666377397
5.95558949152207
5.96250987778931
5.96941866883116
5.97631556593458
5.98321320811302
5.99012510859443
5.99704901594179
6.00398787546928
6.01094589661613
6.01791281001557
6.02487909210733
6.03183491398147
6.03876997625943
6.04568121622683
6.05258053247428
6.05947014790043
6.066363424598
6.07327417417968
6.08020348810772
6.08714878670933
6.09410463240133
6.10106598639238
6.10802047544226
6.11495987796827
6.12188283732816
6.12879083276064
6.13568621599826
6.14257692617653
6.14947452845178
6.1563904104618
6.16332400246113
6.17026698453361
6.17721801663963
6.18416880355285
6.19111022901087
6.19804272136933
6.20496009069932
6.21185966022135
6.21874936545993
6.22564433380481
I left in a number of ‘troubleshooting’ steps (mostly the disp calls) for the time being. You can safely remove them (and the bounds and size calls) without functionally altering the code.
The only change you need to make in my original (earlier) code is to add this (or a similar) assignment:
ffmin1 = min(ff,'all')
ff(ff==ffmin1) = 0; % Replace Miissing Data With ‘0’
after defining ‘ff’. Then, everything should work.
An alternative ‘ffmin1’ determination is:
ffmin1 = min(ff(:))
since I’m not certain what version of MATLAB you’re using.
.
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