Awesome, thanks! This gets me most of the way there, but I noticed a discrepancy. It looks like the result of what you gave me isn’t exactly the same as if I multiplied each January by 31, each February by 28.25, etc. — except, for some reason, for every July and December, which work perfectly. I made a few variations:
As you can see in the results printed below my signature, BA_v3 works correctly. Thanks for your help! Sam yes? list/prec=7 ba_rate[i=@sum,j=@sum,l=1:48], BA_v1[i=@sum,j=@sum,l=1:48], BA_v2[i=@sum,j=@sum,l=1:48], BA_v3[i=@sum,j=@sum,l=1:48] DATA SET: ./land_month_meanFxAvgProper_1_1501-1723_BArate.nc TIME: 01-JAN-1501 00:00 to 01-JAN-1505 00:00 JULIAN LONGITUDE: 0E to 0E(360) (XY summed) LATITUDE: 90S to 90N (XY summed) Column 1: BA_RATE is Burned area (km2/day) Column 2: BA_V1 is BA_RATE * DAYS_IN_MONTHS[GT=BA_RATE] Column 3: BA_V2 is BA_RATE * DAYS_IN_MONTHS2[GT=BA_RATE] Column 4: BA_V3 is BA_RATE * DAYS_IN_MONTHS2[GT=BA_RATE@ASN] BA_RATE BA_V1 BA_V2 BA_V3 16-JAN-1501 12 / 1: 80.44 2488. 2486. 2494. 15-FEB-1501 00 / 2: 2338.79 66202. 65719. 65486. 16-MAR-1501 12 / 3: 9201.38 285098. 284947. 285243. 16-APR-1501 00 / 4: 13499.57 405199. 405420. 404987. 16-MAY-1501 12 / 5: 14870.32 460746. 460503. 460980. 16-JUN-1501 00 / 6: 16933.16 508261. 508538. 507995. 16-JUL-1501 12 / 7: 21494.46 666328. 666328. 666328. 16-AUG-1501 12 / 8: 27994.54 867391. 866932. 867831. 16-SEP-1501 00 / 9: 33873.29 1016731. 1017286. 1016199. 16-OCT-1501 12 / 10: 25794.03 799209. 798787. 799615. 16-NOV-1501 00 / 11: 21719.95 651940. 652296. 651599. 16-DEC-1501 12 / 12: 20904.34 648035. 648035. 648035. 16-JAN-1502 12 / 13: 22102.71 684201. 682983. 685184. 15-FEB-1502 00 / 14: 22847.21 646185. 641997. 639722. 16-MAR-1502 12 / 15: 22879.40 709089. 708527. 709261. 16-APR-1502 00 / 16: 22050.57 661683. 662225. 661517. 16-MAY-1502 12 / 17: 21413.66 663662. 663136. 663823. 16-JUN-1502 00 / 18: 18749.40 562623. 563084. 562482. 16-JUL-1502 12 / 19: 23282.84 721768. 721768. 721768. 16-AUG-1502 12 / 20: 31219.10 967558. 966790. 967792. 16-SEP-1502 00 / 21: 32838.06 985389. 986196. 985142. 16-OCT-1502 12 / 22: 29327.18 908922. 908201. 909143. 16-NOV-1502 00 / 23: 18861.25 565979. 566443. 565837. 16-DEC-1502 12 / 24: 24675.93 764954. 764954. 764954. 16-JAN-1503 12 / 25: 24545.29 760382. 759302. 760904. 15-FEB-1503 00 / 26: 23400.15 661281. 679352. 655204. 16-MAR-1503 12 / 27: 24048.15 745493. 745493. 745493. 16-APR-1503 00 / 28: 25808.78 774263. 774263. 774263. 16-MAY-1503 12 / 29: 19201.62 595250. 595250. 595250. 16-JUN-1503 00 / 30: 17855.02 535651. 535651. 535651. 16-JUL-1503 12 / 31: 16090.21 498797. 498797. 498797. 16-AUG-1503 12 / 32: 21976.96 681286. 681286. 681286. 16-SEP-1503 00 / 33: 27334.76 820043. 820043. 820043. 16-OCT-1503 12 / 34: 28825.21 893581. 893581. 893581. 16-NOV-1503 00 / 35: 30561.32 916840. 916840. 916840. 16-DEC-1503 12 / 36: 28564.04 885485. 885485. 885485. 16-JAN-1504 12 / 37: 25712.30 797081. 797081. 797081. 15-FEB-1504 12 / 38: 25385.38 717972. 712028. 736176. 16-MAR-1504 12 / 39: 24186.73 749210. 749012. 749789. 16-APR-1504 00 / 40: 22441.96 673796. 673980. 673259. 16-MAY-1504 12 / 41: 19744.92 611620. 611458. 612092. 16-JUN-1504 00 / 42: 16672.43 500572. 500708. 500173. 16-JUL-1504 12 / 43: 15625.86 484402. 484402. 484402. 16-AUG-1504 12 / 44: 20372.97 631075. 630908. 631562. 16-SEP-1504 00 / 45: 26970.94 809773. 809994. 809128. 16-OCT-1504 12 / 46: 28341.75 877916. 877684. 878594. 16-NOV-1504 00 / 47: 27011.61 810994. 811216. 810348. 16-DEC-1504 12 / 48: 26251.07 813783. 813783. 813783. yes? list BA_v1[i=@sum,j=@sum,l=1:48]/ba_rate[i=@sum,j=@sum,l=1:48], BA_v2[i=@sum,j=@sum,l=1:48]/ba_rate[i=@sum,j=@sum,l=1:48], BA_v3[i=@sum,j=@sum,l=1:48]/ba_rate[i=@sum,j=@sum,l=1:48] DATA SET: ./land_month_meanFxAvgProper_1_1501-1723_BArate.nc TIME: 01-JAN-1501 00:00 to 01-JAN-1505 00:00 JULIAN LONGITUDE: 0E to 0E(360) LATITUDE: 90S to 90N Column 1: EX#1 is BA_V1[I=@SUM,J=@SUM,L=1:48]/BA_RATE[I=@SUM,J=@SUM,L=1:48] Column 2: EX#2 is BA_V2[I=@SUM,J=@SUM,L=1:48]/BA_RATE[I=@SUM,J=@SUM,L=1:48] Column 3: EX#3 is BA_V3[I=@SUM,J=@SUM,L=1:48]/BA_RATE[I=@SUM,J=@SUM,L=1:48] EX#1 EX#2 EX#3 16-JAN-1501 12 / 1: 30.93 30.90 31.00 15-FEB-1501 00 / 2: 28.31 28.10 28.00 16-MAR-1501 12 / 3: 30.98 30.97 31.00 16-APR-1501 00 / 4: 30.02 30.03 30.00 16-MAY-1501 12 / 5: 30.98 30.97 31.00 16-JUN-1501 00 / 6: 30.02 30.03 30.00 16-JUL-1501 12 / 7: 31.00 31.00 31.00 16-AUG-1501 12 / 8: 30.98 30.97 31.00 16-SEP-1501 00 / 9: 30.02 30.03 30.00 16-OCT-1501 12 / 10: 30.98 30.97 31.00 16-NOV-1501 00 / 11: 30.02 30.03 30.00 16-DEC-1501 12 / 12: 31.00 31.00 31.00 16-JAN-1502 12 / 13: 30.96 30.90 31.00 15-FEB-1502 00 / 14: 28.28 28.10 28.00 16-MAR-1502 12 / 15: 30.99 30.97 31.00 16-APR-1502 00 / 16: 30.01 30.03 30.00 16-MAY-1502 12 / 17: 30.99 30.97 31.00 16-JUN-1502 00 / 18: 30.01 30.03 30.00 16-JUL-1502 12 / 19: 31.00 31.00 31.00 16-AUG-1502 12 / 20: 30.99 30.97 31.00 16-SEP-1502 00 / 21: 30.01 30.03 30.00 16-OCT-1502 12 / 22: 30.99 30.97 31.00 16-NOV-1502 00 / 23: 30.01 30.03 30.00 16-DEC-1502 12 / 24: 31.00 31.00 31.00 16-JAN-1503 12 / 25: 30.98 30.93 31.00 15-FEB-1503 00 / 26: 28.26 29.03 28.00 16-MAR-1503 12 / 27: 31.00 31.00 31.00 16-APR-1503 00 / 28: 30.00 30.00 30.00 16-MAY-1503 12 / 29: 31.00 31.00 31.00 16-JUN-1503 00 / 30: 30.00 30.00 30.00 16-JUL-1503 12 / 31: 31.00 31.00 31.00 16-AUG-1503 12 / 32: 31.00 31.00 31.00 16-SEP-1503 00 / 33: 30.00 30.00 30.00 16-OCT-1503 12 / 34: 31.00 31.00 31.00 16-NOV-1503 00 / 35: 30.00 30.00 30.00 16-DEC-1503 12 / 36: 31.00 31.00 31.00 16-JAN-1504 12 / 37: 31.00 31.00 31.00 15-FEB-1504 12 / 38: 28.28 28.05 29.00 16-MAR-1504 12 / 39: 30.98 30.97 31.00 16-APR-1504 00 / 40: 30.02 30.03 30.00 16-MAY-1504 12 / 41: 30.98 30.97 31.00 16-JUN-1504 00 / 42: 30.02 30.03 30.00 16-JUL-1504 12 / 43: 31.00 31.00 31.00 16-AUG-1504 12 / 44: 30.98 30.97 31.00 16-SEP-1504 00 / 45: 30.02 30.03 30.00 16-OCT-1504 12 / 46: 30.98 30.97 31.00 16-NOV-1504 00 / 47: 30.02 30.03 30.00 16-DEC-1504 12 / 48: 31.00 31.00 31.00 On Oct 22, 2014, at 6:11 PM, Ansley Manke <ansley.b.manke@xxxxxxxx> wrote:
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