Interpolation of a table (N dependent over M independent) variables

I have the given table:

% AoA	 vel	  CDa	        CLa	         CSa	          Cmxa	        Cmya	        Cmza

-40	 5	  1.14668	-1.13299	 0.007386	 -0.00189	 0.00032	-0.06785
-30	 5	  0.792945	-1.05145	 0.001173	  0.000308	-0.00066	-0.05284
-20	 5	  0.46091       -0.9383	         0.004354	 -0.00228	-0.00047	-0.03891
-10	 5	  0.176381	-0.70155	-0.0037	          0.000787	 2.33E-07	-0.02382
 10	 5	  0.189115	1.388965	-0.00634	  0.002562	-0.00117	 0.024032
 15	 5	  0.309158	1.551853	-0.00271	  5.05E-05	 0.000825	 0.033537
 20	 5	  0.640063	1.489591	-0.02716	  0.006253	-0.00117	 0.038003
 30	 5	  0.843689	1.382815	 0.004803	 -0.00204	-0.00216	 0.050888
 45	 5	  1.435123	1.337931	-0.01323	  0.001252	-0.00139	 0.069426

-40	10	  1.135769	-1.12232	 0.008136	 -0.00338	 0.000516	-0.13411
-30	10	  0.786587	-1.04173	 0.000678	  0.001555	-0.00207	-0.10408
-20	10	  0.454954	-0.93194	 0.002149	 -0.00573	-0.00102	-0.07623
-10	10	  0.17226	-0.70207	-0.0055	          0.001301	 0.000137	-0.04808
 10	10	  0.185599	 1.40734	-0.0057	          0.001352	-0.00197	 0.047792
 15	10	  0.302297	 1.554765	-0.00804	  0.000129	-0.00238	 0.068301
 20	10	  0.635433	 0.917047	-0.02498	  0.007075	-0.00257	 0.089213
 30	10	  0.840859	 1.379861	 0.008731	 -0.00259	-0.00295	 0.101972
 45	10	  1.600783	 1.628605	-0.00913	  0.000973	-0.0029	         0.073734

-40	15	  1.134235	-1.12156	 0.008302	 -0.00298	 0.00131	-0.13368
-30	15	  0.78341	-1.0394	         0.0019	          0.001564	-0.00192	-0.10364
-20	15	  0.456106	-0.93554	 0.003156	 -0.00523	-0.00066	-0.07602
-10	15	  0.17026	-0.69772	-0.0069	          0.00247	 0.000491	-0.04811
 10	15	  0.183753	 1.411952	-0.00514	  0.001282	-0.0019	         0.047676
 15	15	  0.306336	 1.572716	-0.02108	 -0.00823	-0.00079	 0.068636
 20	15	  0.446206	 1.49619	-0.02127	  0.004692	-0.00195	 0.075722
 30	15	  0.794182	 1.287936	 0.024425	 -0.00247	-0.00354	 0.119258
 45	15	  1.564058	 1.411367	-0.01153	  0.00088	-0.0027	         0.158261

-40	20	  1.130857	-1.11942	 0.011118	 -0.00251	 0.001287	-0.13325
-30	20	  0.783355	-1.03969	 0.001792	  0.001849	-0.00195	-0.10357
-20	20	  0.453382	-0.93397	 0.004538	 -0.00527	-0.00116	-0.07611
-10	20	  0.169203	-0.70222	-0.00304	  0.001263	-0.00043	-0.04776
 10	20	  0.182575	 1.414132	-0.00465	  0.001476	-0.00181	 0.047619
 15	20	  0.302945	 1.557986	-0.02611	 -0.01088	-0.00131	 0.069002
 20	20	  0.441594	 1.667185	-0.03727	  0.018395	-0.001	         0.085187
 30	20	  0.833129	 1.367756	 0.021856	 -0.00202	-0.00372	 0.100927
 45	20	  1.414648	 1.322671	-0.01084	  0.000713	-0.00222	 0.137053

-40	25	  1.130254	-1.11854	 0.011035	 -0.0026	 0.001291	-0.13307
-30	25	  0.78033	-1.03801	 0.006084	  0.001668	-0.00065	-0.10342
-20	25	  0.454824	-0.93126	-3.40E-06	 -0.00521	-0.00019	-0.07559
-10	25	  0.167459	-0.69818	-0.0036	          0.002762	 0.000251	-0.04732
 10	25	  0.181822	 1.416205	-0.00467	  0.001572	-0.00181	 0.047616
 15	25	  0.307287	 1.595218	-0.01342	 -0.00625	-0.00398	 0.072986
 20	25	  0.449001	 1.491301	-0.03155	  0.01809	-0.00051	 0.074918
 30	25	  0.853412	 1.258704	 0.017987	 -0.00277	-0.00635	 0.089384
 45	25	  1.543683	 1.460518	-0.01509	 -0.00111	-0.00411	 0.14807

-40	30	  1.125591	-1.11304	 0.00975	 -0.0033	 0.001251	-0.13249
-30	30	  0.780225	-1.0392	         0.007942	  0.002208	-0.00092	-0.10343
-20	30	  0.453074	-0.9373	         0.002856	 -0.00568	-0.00058	-0.07605
-10	30	  0.166888	-0.7011	        -0.00378	  0.002011	 6.89E-05	-0.04708
 10	30	  0.181113	 1.41787	-0.00429	  0.001623	-0.00172	 0.04744
 15	30	  0.309359	 1.560654	-0.01769	 -0.00408	-0.00315	 0.073177
 20	30	  0.424315	 1.507518	-0.0368	          0.019651	-0.0007	         0.076681
 30	30	  0.829189	 1.368019	 0.008733	 -0.00308	-0.00554	 0.100652
 45	30	  1.416077	 1.324257	-0.01235	 -0.00132	-0.00327	 0.137079

-40	35	  1.128033	-1.11649	 0.009527	 -0.00154	 0.000553	-0.06634
-30	35	  0.781222	-1.04085	 0.003488	  0.001143	-0.00094	-0.05175
-20	35	  0.452797	-0.93978	 0.003031	 -0.0029	-0.0004	        -0.03791
-10	35	  0.16714	-0.70461	 6.22E-05	  0.000711	-0.00057	-0.02364
 10	35	  0.180499	 1.418791	-0.00459	  0.000791	-0.0009	         0.02378
 15	35	  0.303655	 1.545379	-0.01416	 -0.00088	-0.0001	         0.035761
 20	35	  0.477813	 1.317009	-0.03607	  0.011042	 0.000731	 0.036779
 30	35	  0.833212	 1.306725	-0.01089	 -0.00047	-0.00033	 0.048257
 45	35	  1.387943	 1.286388	-0.01112	 -0.00306	-0.00334	 0.060734

-40	40	  1.130903	-1.12077	 0.008338	 -0.00266	 0.001092	 -0.13321
-30	40	  0.779686	-1.03895	 0.007888	  0.002269	-0.00089	 -0.10334
-20	40	  0.450783	-0.93033	 0.000821	 -0.00456	 0.00064	 -0.07527
-10	40	  0.166252	-0.70388	-0.00162	  0.001372	-0.00043	 -0.04719
 10	40	  0.180017	 1.420129	-0.00468	  0.001581	-3.60E-05	  0.047408
 15	40	  0.302535	 1.530176	-0.00861	  0.005485	-0.00278	  0.071903
 20	40	  0.447166	 1.489379	-0.03367	  0.020146	 0.003072	  0.074659
 30	40	  0.831837	 1.375903	-0.01245	  7.12E-05	-0.00282	  0.10027
 45	40	  1.41966	 1.329288	-0.00866	 -0.00745	-0.00655	  0.137312

Independent variables: ,

Dependent variables: , , , , ,

So far I have the solution for the any of dependent variables, e.g. for :

AoA = [-40, -30, -20, -10, 10, 15, 20, 30, 45]; % 9 elements
vel = [5, 10, 15, 20, 25, 30, 35, 40]; % 8 elements
CDa = [1.14668, 0.792945, 0.46091, 0.176381, 0.189115, 0.309158, 0.640063, 0.843689, 1.435123,
       1.135769, 0.786587, 0.454954, 0.17226, 0.185599, 0.302297, 0.635433, 0.840859, 1.600783,
       1.134235, 0.78341, 0.456106, 0.17026, 0.183753, 0.306336, 0.446206, 0.794182, 1.564058,
       1.130857, 0.783355, 0.453382, 0.169203, 0.182575, 0.302945, 0.441594, 0.833129, 1.414648,
       1.130254, 0.78033, 0.454824, 0.167459, 0.181822, 0.307287, 0.449001, 0.853412, 1.543683,
       1.125591, 0.780225, 0.453074, 0.166888, 0.181113, 0.309359, 0.424315, 0.829189, 1.416077,
       1.128033, 0.781222, 0.452797, 0.16714, 0.180499, 0.303655, 0.477813, 0.833212, 1.387943,
       1.130903, 0.779686, 0.450783, 0.166252, 0.180017, 0.302535, 0.447166, 0.831837, 1.41966]; % 72 elements
CDa_i = griddata (AoA, vel, CDa, -35, 7); % interpolated however only one column, namely CDa

So I need to transform every dependent variable (column) into 1D-array size of (NxM) and use griddata

This could be done for all columns but the process is tedious and error-prone.

I need somewhat like: [, , , , , ] = interpNxM(, );

The question: Does a simpler solution exist?