CHIRPS Reality Checks - Revision history

Harrison: /* June 2026 */

2026-07-15T15:42:48Z

June 2026

Harrison: /* June 2026 */

2026-07-15T15:38:11Z

June 2026

Harrison: /* June 2026 */

2026-07-15T15:07:59Z

June 2026

Harrison: /* June 2026 */

2026-07-15T15:03:06Z

June 2026

Harrison: /* May 2026 */

2026-07-15T15:02:44Z

May 2026

Harrison: /* May 2026 */

2026-07-15T15:02:35Z

May 2026

Harrison: /* May 2026 */

2026-07-15T15:02:25Z

May 2026

Harrison: /* May 2026 */

2026-06-16T01:06:52Z

May 2026

Harrison: /* May 2026 */

2026-06-16T01:02:38Z

May 2026

Daniella: /* May 2026 */

2026-06-12T23:00:07Z

May 2026

← Older revision		Revision as of 15:42, 15 July 2026
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	'''Ethiopia''' 3D-PAWS have started to be deployed in Ethiopia. There are two 3D-PAWS stations currently reporting. These stations have made it into CHIRPS Version 2 but a new epoch will need to be made before they are in CHIRPS Version 3.		'''Ethiopia''' 3D-PAWS have started to be deployed in Ethiopia. There are two 3D-PAWS stations currently reporting. These stations have made it into CHIRPS Version 2 but a new epoch will need to be made before they are in CHIRPS Version 3.
		+
		+	'''Columbia''' Interesting differences in CHIRP v2 and v3 in western Columbia. CHIRP v2 and v3 and CHIRPS3 agree on signal (anomaly pattern) but CHIRP2 is drier than the other three products. A similar result is also noted in St Vincent & the Grenadines.

	'''Rchecks plots'''		'''Rchecks plots'''

@@ Line 30: / Line 30: @@
 '''CHIRPS v2: Final versus Prelim''' This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/v2/archive/Difference_global_202606.png map] shows the difference between Prelim data and Final data, for June 2026 CHIRPS v2. This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/v2/archive/PercentDifference_global_202606.png map] shows the ratio of values.
-'''Ethiopia''' 3D-PAWS have started to be deployed in Ethiopia. There are two 3D-PAWS stations currently reporting. These stations have made it into Version 2 but a new epoch will need to be made before they are in Version 3.
+'''Ethiopia''' 3D-PAWS have started to be deployed in Ethiopia. There are two 3D-PAWS stations currently reporting. These stations have made it into CHIRPS Version 2 but a new epoch will need to be made before they are in CHIRPS Version 3.
 '''Rchecks plots'''

← Older revision		Revision as of 15:07, 15 July 2026
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	'''CHIRPS v2: Final versus Prelim''' This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/v2/archive/Difference_global_202606.png map] shows the difference between Prelim data and Final data, for June 2026 CHIRPS v2. This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/v2/archive/PercentDifference_global_202606.png map] shows the ratio of values.		'''CHIRPS v2: Final versus Prelim''' This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/v2/archive/Difference_global_202606.png map] shows the difference between Prelim data and Final data, for June 2026 CHIRPS v2. This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/v2/archive/PercentDifference_global_202606.png map] shows the ratio of values.
		+
		+	'''Ethiopia''' 3D-PAWS have started to be deployed in Ethiopia. There are two 3D-PAWS stations currently reporting. These stations have made it into Version 2 but a new epoch will need to be made before they are in Version 3.

	'''Rchecks plots'''		'''Rchecks plots'''

@@ Line 32: / Line 32: @@
 '''Rchecks plots'''
-Global: Another June with much higher CHIRPSv3 than CHIRP3. The global CHIRP3 mean appears to be the lowest June values estimated in the CHIRP3 global June 1981-present timeseries. Based on anomaly images in EWX, it seems like the low CHIRP3 value for June 2026 is largely driven by estimated large negative rainfall anomalies in India, southeast Asia, and Maritime Continent. Stations showed that in several influential locations these were underestimates (by CHIRP3), and the blending of these stations substantially increased values in CHIRPS3. ([https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/rchecks/monthly_compares/chirpsV3.Global.stats.2026.06.png Link])
+Global: Another June with much higher CHIRPS3 than CHIRP3. The global CHIRP3 mean appears to be the lowest June values estimated in the CHIRP3 global June 1981-present timeseries. Based on anomaly images in EWX, it seems like the low CHIRP3 value for June 2026 is largely driven by estimated large negative rainfall anomalies in India, southeast Asia, and Maritime Continent. Stations showed that in several influential locations these were underestimates (by CHIRP3), and the blending of these stations substantially increased values in CHIRPS3. ([https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/rchecks/monthly_compares/chirpsV3.Global.stats.2026.06.png Link])
 Asia: As noted in the global summary, June 2026 was one of the driest on record for Asia during the month of June according to CHIRP, but made considerably wetter by stations (CHIRPS). The corresponding difference between CHIRPS and CHIRP is the largest on record for the month of June.

@@ Line 24: / Line 24: @@
 ''
 ==R-Checks Monthly Highlights==
-=== May 2026 ===
+=== June 2026 ===
 '''CHIRPS v3: Final versus Prelim''' This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/archive/Difference_global_202606.png map] shows the difference between Prelim data and Final data, for June 2026 CHIRPS v3. This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/archive/PercentDifference_global_202606.png map] shows the ratio of values.

← Older revision		Revision as of 01:06, 16 June 2026
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	'''South Africa''' A series of storms impacted South Africa in early May, with persistent heavy rainfall impacting western and eastern Cape provinces, destroying infrastructure and upending lives. South African authorities declared a [https://apnews.com/article/south-africa-floods-storms-death-disaster-ee9de87f657d64586da774b3a1f0bba8 national disaster] following the severe flooding. The heavy rainfall was well captured by CHIRP3 and CHIRPS3.		'''South Africa''' A series of storms impacted South Africa in early May, with persistent heavy rainfall impacting western and eastern Cape provinces, destroying infrastructure and upending lives. South African authorities declared a [https://apnews.com/article/south-africa-floods-storms-death-disaster-ee9de87f657d64586da774b3a1f0bba8 national disaster] following the severe flooding. The heavy rainfall was well captured by CHIRP3 and CHIRPS3.
		+
		+	'''Rchecks plots''' May 2026 was characterized by exceptionally high localized rainfall totals, with maximum (wettest pixel) CHIRPS rainfall accumulations among the highest observed in the historical records for [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/rchecks/monthly_compares/chirpsV3.Southern_Africa.stats.2026.05.png Southern Africa]. Plots are available [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/rchecks/monthly_compares/?C=M&O=D here].

	=== April 2026 ===		=== April 2026 ===

@@ Line 30: / Line 30: @@
 '''CHIRPS v2: Final versus Prelim''' This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/v2/archive/Difference_global_202605.png map] shows the difference between Prelim data and Final data, for May 2026 CHIRPS v2. This [https://data.chc.ucsb.edu/products/CHIRPS/v3.0/diagnostics/chirps_minus_prelim/v2/archive/PercentDifference_global_202605.png map] shows the ratio of values.
-'''Data notes''' We've been seeing an issue in which CHIRP3's very low values + blending procedure leads to wet stations not making their influence in CHIRPS3. We've noted this as occurring in winter-early spring and mainly in Eurasia. Here in May 2026 data we see that this has occurred to some extent in that region, but to a much lesser degree. One place we see it is in May 2026 is in central Ukraine. In central Ukraine, IMERG Late and CHIRP/S 2 show avg to abv avg rain. In CHIRP/S 3 there are a few locations with average to above average rain, though these don't align with station locations, and in between these are low values. It's not very noticable in data fields, but one can see it in anomalies. Good news: The CHIRP3 values in May 2026 here and across Eurasia are large enough (far enough from near zero) that the station values ARE having influence on CHIRPS3, ARE raising the CHIRPS3 estimates, and enabling CHIRPS3 to show a more accurate depiction of rains. Check out April 2026 vs. May 2026 data values in Rchecks, CHIRP3, IMERG. Check out the CHIRP3 anomalies vs. the CHIRPS3 Rchecks anomalies to see what a difference it makes, when CHIRP3 values are at least moderate vs. nero zero/zero, in getting stations to have an influence. There are still locations where CHIRP3 is 0 mm and so is CHIRPS3, depite stations with ~ 30mm, e.g.  in parts of northern Mongolia and northwest China, but the situation is far less extreme than in some of the previous cases. Which underscores the seasonal characteristic of the issue. The algorithm for CHIRP3, including the CHIRP dependence on cold cloud duration and the omission of an intercept term that CHIRP2 had (which enabled CHIRP2 to lean into climatology despite CCD being extremely low), means that CHIRP3 values are more often 0mm instead of drizzly in these types of cases. The current station and CHIRP blending procedure doesn't overcome such low CHIRP values.
+'''Data notes:''' We've been seeing a geographically focused issue in which CHIRP3's very low values + blending procedure leads to wet stations not making their influence in CHIRPS3, primarily in winter-early spring and mainly in Eurasia. In May 2026 CHIRPS final data we see that this occurred to some extent in that region, but to a much lesser degree. One place we see it is in May 2026 is in central Ukraine. In central Ukraine, IMERG Late and CHIRP/S 2 show average to above average rain. In CHIRP/S v3 there are a few locations with average to above average rain, though these don't align with station locations, and in between these are low values. The helpful situation is that CHIRP3 values in May 2026 here, and across Eurasia are large enough (far enough from near zero) that the station values are having a substantial influence on CHIRPS3 and are enabling CHIRPS3 to show a more accurate depiction of rains. There are still locations where CHIRP3 is 0 mm and so is CHIRPS3, despite stations with ~ 30mm, e.g.  in parts of northern Mongolia and northwest China, but the situation is far less extreme than in some of the previous cases. Based on previous cases, the issue is seasonally focused. The algorithm for CHIRP3 is in part at fault. CHIRP depends on cold cloud duration estimates, and in the CHIRPv2 to CHIRPv3 algorithm change, the intercept term was omitted in CHIRPv3. This term enabled CHIRP2 to lean into climatology, despite CCD being extremely low. In CHIRPv3, values are more often 0 mm instead of drizzly in such cases. The current station and CHIRPv3 blending procedure doesn't overcome such low CHIRP values.
 '''China''' May 2026 rainfall was well above average in Hunan, mountainous province in southwestern China, where more than 30,000 people were evacuated and several people were [https://www.nytimes.com/2026/05/20/world/asia/china-flood-rain-deaths.html reported] missing. The heavy rainfall was captured by CHIRPS and CHIRP.