Alaska Rainfall Record

Over the last several years, we have embarked on a long, strange journey to fix an error in the Alaska climate record. Not just any error, but a state record error. The culmination of this process is an article in the August 2014 issue of the Bulletin of the American Meteorological Society (BAMS). The .pdf document can be viewed directly from their website here. The document is free of charge and there is no registration required to access the article. The next several paragraphs will describe what happened, how it is being corrected, and what happens next.

Back in 1982, in the Native Village of Angoon, in Southeast Alaska, a Cooperative weather observer recorded 15.20" of rain in a single day. This remarkable value was entered on to a monthly form and submitted to NOAA (via mail) for transcription and archiving. Once this value was entered into the system, any database query could identify it as the single greatest 24-hour precipitation event in Alaska history. Because Angoon is located in Southeast Alaska, one of the wettest regions in the world, it was assumed that this staggering total was entirely plausible – even though Angoon lies in a rain shadow and is the driest location in all of Southeast Alaska.

Figure 1. Location of Angoon, Alaska.

Figure 2. Aerial view of Angoon from fixed-wind aircraft.

By sheer coincidence, in 2009 I began working for a company that was involved in drafting an Environmental Impact Statement for a proposed land-based airport in Angoon. When I came across this 15.20" precipitation event, something didn't seem right about it. Doing a little digging I came across the original observer form from 1982. The precipitation total on October 12th clearly indicates a reading of 15.20".

Figure 3. Scanned observation form from Angoon for October 1982.

Knowing that the average annual precipitation for Angoon was around 35" - 40", it did not seem possible that nearly half the annual precipitation could fall on a single day. That only happens in desert areas. In fact, when all of the precipitation totals for a 12-month period were added together for Angoon in 1982, it came out to over 200". Clearly something was amiss. But what was it?

Surely this type of outlier would have been noticed in the previous 30 years, right? Perhaps it was. However, no one had fully documented the process. Therefore, I set out to write-up my findings and pass it along to the NWS in Juneau. As fate would have it, Carl Trypaluk, who was working with NOAA in Silver Spring, Maryland, had also noticed this observation while developing new precipitation recurrence intervals as part of the NOAA Atlas 14 project. Carl wrote up his findings and also sent it to the NWS in Juneau shortly before I had done so. Since we both had researched the same error at nearly the same time, we decided to join forces and investigate further.

What happened

As luck would have it, the gentleman who recorded the precipitation at the time is still alive and was willing to talk with me about it from his home in Ketchikan. What kind of luck is that! It turns out that the long-time observer in Angoon had passed away the previous year and the gentleman now in Ketchikan continued the observations to preserve the data continuity. Unfortunately, before he was able to be trained by the NWS staff in Juneau, he collected several months of precipitation data using an incorrect measurement technique – which magnified all precipitation readings by a factor of 10. In hindsight, the 15.20" should have been 1.52". 

Figure 4. Location of the observation station in 1982. The station burned down c. 1985. The red box shows the approximate footprint of the building that the station was next to.

Changing the record

Purging a record from the books is not an easy task. There is a tremendous inertia that must be overcome. The National Climate Data Center (NCDC) has a procedure for evaluating state records. It involves the formation of an ad hoc committee to evaluate all relevant information. The committee then decides whether or not to accept or reject the validity of the record. In the case of Angoon, a committee was formed and the recommendation of the committee was to strike the Angoon record from the books. Now, the Director of the NCDC must sign-off on the recommendation. Presumably that will happen sooner rather than later. However, changing the record is a two-step process. Step 1 was resolving the status of the Angoon record. Step 2 is declaring a new state precipitation record. Step 1 is nearly complete, but Step 2 .....

Seward, Alaska

In the Olympics, when the winner of an event is disqualified after the fact, the second place finisher is promoted into first place and is awarded the gold medal. Once the Angoon record is moved out of first place, who gets to move up? The answer is Seward. They received 15.05" of rain on October 10, 1986.

On October 3rd, the State Climate Extreme's Committee concurred with our findings and placed Seaward at the top of Alaska's precipitation record list.

Figure 5. Rainfall totals in Southcentral Alaska on October 10, 1986. The value for Seward is circled in red.

How do the people of Seward feel about this? There's a saying that as long as the weather is extreme, you might as well set a record. However, the Chamber of Commerce might not want the designation of having the wettest day in Alaska's history. Whatever the case, records are an important part of the climate history of Alaska and every other place in the world. We need to know the bounds of the climate system to make effective public policy decisions. Fixing this record is a small contribution toward that goal.

Note: Figures 1, 2, 4, and 5 are copyrighted by Brian Brettschneider. Figure 3 is courtesy of NOAA.

Alaska September Daily Temperature Change

These slider images show the 30-day change in temperatures between the beginning and end of Septemper. The top image shows the high (max) temperature change and the bottom image shows the low (min) temperature change.

Wettest Month of the Year

When is the wettest month of the year? The answer to that question depends greatly on where you live. If you live along the West Coast, the answer is probably one of the winter months. If you live in the Great Plains, the answer is likely one of the summer months. There are a myriad of reasons for this temporal and geographical disparity. They include: solar heating, jet stream position, sea surface temperatures, upper level patterns, and so on. In some instances, the distribution of annual precipitation is quite uniform and no straightforward seasonal pattern exists. In those cases, the variation between nearby stations can appear chaotic.

Methodology:

To make the maps shown in the figures below, we used the NCDC 1981-2010 normal monthly precipitation values for 8,535 stations in the 50 states. For each station, the month with the greatest normal precipitation was identified. There were 98 stations that had a tie for the wettest month and 1 station with a three-way tie for the wettest month. In the monthly maps that begin with Figure 4, stations that were tied are shown in any month where the tie exists.

Nationwide Wettest Months and Seasons:

Before we begin with the map set, please take the opportunity to download a Google Earth file with all 8,535 stations that are color coded by the wettest month. Each station's monthly precipitation data can be viewed by clicking on the station's marker. 

The first 3 figures show the wettest climatological season (Figure 1) and the monthly peak precipitation values (Figures 2, 3).

Wettest Season:

If we define the seasons by traditional climatological boundaries (Dec-Feb, Mar-May, Jun-Aug, and Sep-Nov) and add up the normal precipitation values for each of those time periods, we can easily identify which season is the wettest. Figure 1 shows the result of that analysis. Since there are only four categories, the seasonal boundaries are quite easy to discern. The West Coast has a winter precipitation peak and most of the rest of the country sees a summer peak – with the notable exception of an area bounded by Central Texas to the Ohio River Valley to the Deep South.

Figure 1. Dot map of wettest climatological season of the year. 8535 stations were used to make this map.

Wettest Month:

Unlike the seasonal map, the combined monthly maps are much more difficult to visualize. That being said, it is important to distinguish between a January peak and a December peak (for example). Figures 2 and 3 show the station breakdown by calendar month – one as a dot map and the other as a color scale (choropleth) map. For example, the Spring maximum noted in Figure 1 is shown in Figure 2 to be nearly entirely composed of stations whose wettest month is May. To the west of the May stations, a large area of June stations exist. While the seasonal map showed a significant break in the region, it is relatively minor when looking at the monthly data. However, a sharp transition to Winter peak values exists to the east of the May stations. Again, having the monthly values is important for this type of assessment.

Figure 2. Dot map of wettest month of the year. 8535 stations were used to make this map. If a station had more than one wettest month, the month closest to its neighbors was chosen.


Figure 3. Continuous map of wettest month of the year.

Individual Months:

Instead of trying to decipher (often) complicated patterns, I though it useful to have an individual map for each month of the year. In the following 12 figures (Figures 4 through 15), each month of the year is pulled out individually. Only those stations with a peak precipitation value in that month are shown. If a station has a tie for the peak month, it is shown on all maps for which a tie exists. 

Figure 4. Stations where the wettest month of the year is January (n=277).

Figure 5. Stations where the wettest month of the year is February (n=332).

Figure 6. Stations where the wettest month of the year is March (n=263).

Figure 7. Stations where the wettest month of the year is April (n=76).

 Figure 8. Stations where the wettest month of the year is May (n=1,881).

Figure 9. Stations where the wettest month of the year is June (n=2,053).

Figure 10. Stations where the wettest month of the year is July (n=1,073).

Figure 11. Stations where the wettest month of the year is August (n=916).

Figure 12. Stations where the wettest month of the year is September (n=438).

Figure 13. Stations where the wettest month of the year is October (n=339).

Figure 14. Stations where the wettest month of the year is November (n=333).

Figure 15. Stations where the wettest month of the year is December (n=653).

Intra-Annual Variability:

In many cases there are substantial difference between wet and dry months. Some stations in California and Alaska receive 60% of their annual precipitation in a three-month window. On the flip-side, many stations in the Northeast and mid-Atlantic have precipitation evenly distributed across all months.

The final map in this blog post (Figure 16) shows the month-to-month variability in precipitation values across the year. To make this map we calculated the difference between the NCDC normal precipitation for each month and compared it to the value that would occur if each month received 1/12th of the annual precipitation. This type of assessment is called a goodness-of-fit test. In this case we used the Chi Square goodness-of-fit-test.

As you can see, some areas have low month-to-month variability and others have quite a bit. I had assumed that all cold regions would have low winter precipitation values due to the moisture capacity of the air being greatly reduced. However, that is only the case in the Northern Great Plains and Alaska – not in New England. The other quite surprising finding is the low month-to-month variability in the Great Basin. Perhaps this is an artifact of multiple synoptic-scale parameters in other regions that all converge in this region.

There are far too many patterns in this map to describe. It is worthy of its own blog post another day!

Figure 16. Intra-annual variability based on monthly totals. Stations with consistent precipitation values throughout the year are shown in green and stations with large month-to-month variation (e.g., distinct wet and dry seasons) are shown in red.

Alaska Temperature Departures From Normal

Heavy Rainfall Trends in the U.S.

Significant research has been conducted in recent years regarding changes in precipitation amounts and patterns in a warming climate. From a theoretical perspective, warmer air holds more moisture so increases in temperature should lead to increases in precipitation. On the flip side, increased temperatures may dry out soils and lakes (sources of moisture), cause air currents to change, or lead to other situations that counter-balance the increase in atmospheric moisture.

A chapter from the recently released National Climate Assessment discusses the trends in long-term heavy precipitation events for the entire U.S. during the last century. In particular, they note how the proportion of annual precipitation from extreme events has increased since the 1950's. The map below shows Figure 2.18 from that report. The map shows that large increases in very heavy precipitation events have been observed in the eastern half of the country.

Figure 1. Map from National Climate Assessment (Figure 2.18) showing the observed change in heavy precipitation events.

I am interested in knowing how the rate of heavy precipitation events has changed at smaller geographical scales. Therefore, I decided to look at all airport stations in the U.S. that have a continuous record dating back multiple decades. In this instance, a beginning point of 1949 was chosen because 207 stations have complete precipitation records between 1949 and 2013 (additional stations with 1 or 2 missing months during the same time period will be added at a future date). This is also a long enough period of time to smooth out increases or decreases due to cyclical climate oscillations with short (<10 year) periods. Cooperative stations were excluded since the time of observation is not consistent from one station to the next and in some cases it changes intra-annually at single stations. Therefore, only airport stations with midnight-to-midnight reporting times were used.

The 207 stations are nicely distributed geographically with a slightly higher density east of the Rocky Mountains and a lower density west of the Rocky Mountain Front Range. The following map (Figure 2) shows the distribution of stations.

Figure 2. Locations of airport stations with complete precipitation data from 1949-2013. A total of 207 stations met the criteria. 

For the purpose of this analysis, we are not studying the temporal spread of singular heavy rain events – just the frequency of high precipitation events. In fact, the year with the highest precipitation event for the 1949-2013 time period at each station is not statistically significant when grouping the years into eight categories. Figure 3 shows the year range of the highest precipitation event for each of the 207 stations. There is a slight tendency for the records to be more frequent in recent years but the significance level (p-value) is only 0.15 and is therefore not significant at the 95% or 90% level. If there was an 85% significance category it would fall within that bound (See Figure 4).

Figure 3. Year when highest calendar day precipitation event for all stations during the 1949-2013 time period was recorded.

Figure 4. Number of maximum precipitation events grouped by year of occurrence for all 207 stations during the 1949-2013 time period.

Methodology

For each station, a linear regression line was fitted to the number of days per year that met or exceeded A) 0.05", B) 0.50", C) 1.00", D) 2.00", and E) 3.00". The first value (0.05") was chosen as a proxy measure for the overall number of rainfall events per year. A smaller value was not used so that future research can extend the analysis to Cooperative stations. Those stations, especially early in their climate records, missed some small precipitation events. The 0.05" value allows us to determine if all precipitation events are increasing or decreasing – not just heavy events.

Once a linear regression was completed for each station at each of the four precipitation thresholds, a probability value (p-value) was computed. The p-value is a statistical measure of significance. A p-value less than 0.05 indicates that there is a less than 5% chance that the statistical trend is random. A p-value less than 0.10 indicates that there is a less than 10% chance that the statistical trend is random. By convention, a p-value greater than 0.10 is considered not statistically significant.

As an example, the Dallas Fort Worth International Airport (GHCN ID: USW00003927) saw a slight decrease in number of days with at least 0.05" of precipitation between 1949 and 2013. However, the p-value was 0.93 – indicating near total randomness in the distribution. Looking at the number of days with at least 0.50", there was an increase over time and the p-value was 0.045. Since this number is less than 0.05, the upward trend is considered significant at the 95% level. The p-value for the trend in days with at least 1.00" was 0.16, for days with at least 2.00" was 0.70, and for days with at least 3.00" was 0.42 – all not significant at the 95% or 90% levels. Collectively, we conclude that the Dallas Fort Worth International Airport has observed a statistically significant increase in the number of days with at least 0.50" of precipitation but all other thresholds were not significant.

Statistical Significance Maps

Instead of plotting percent change (or raw value change) for each station from 1949-2013, I decided to plot statistical significance – using the aforementioned p-value. For example, if a station showed at 20% increase in the number of days with 1.00" or more between 1949 and 2013, 1 or 2 years might be responsible for all of the increase. Therefore, the increase, in that example, is an aberration and not an actual trend. However, we can compute a statistical significance for that station's trend line and report back whether or not the 20% increase was meaningful at the 95% or 90% significance level. For all of the statistical significance calculations and maps, a station must have an average at least 0.5 days per year to calculate a trend – otherwise they are identified as "too few events."

Days per year with at least 0.05"

Most of the stations in the U.S. experienced no statistically significant increase or decrease in the number of days with at least 0.05" of precipitation. A band of stations from the Dakotas to the eastern Great Lakes saw statistically significant increases and some areas in the Southeast saw statistically significant decreases but most of the U.S. was nondescript.

Figure 5. Significance map of trend in number of days with 0.05" of precipitation or greater during the 1949-2013 time period.

Days per year with at least 0.50"

Using a threshold of 0.50", patterns begin to emerge. Many stations from northern Texas to the Dakotas and then eastward to include the entirely of New England saw a statistically significant increase in the number of days with at least 0.50" of precipitation. Much of the West consistently recorded a decrease in the number of days with 0.50" of precipitation but only a few stations were statistically significant.

Figure 6. Significance map of trend in number of days with 0.50" of precipitation or greater during the 1949-2013 time period.

Days per year with at least 1.00"

The statistical significance pattern is even more apparent when looking at days with at least 1.00" of precipitation. Nearly 90% of stations east of the Rocky Mountains saw an increase in the number of 1.00" precipitation days and approximately half of those stations met the 95% statistical significance threshold. Notice that some stations in the Intermountain West receive too few days per year (<0.5) to be included in the analysis.

Figure 7. Significance map of trend in number of days with 1.00" of precipitation or greater during the 1949-2013 time period.

Days per year with at least 2.00"

At the 2.00" threshold, the trend direction (positive or negative) and the significance levels are not nearly as distinct as they were for the 0.50" and 1.00" events. Nevertheless, a clear pattern exists in the northeastern portion of the country and a strong majority of stations east of the Rocky Mountains saw an increase in the number of days with at least 2.00" of precipitation. West of the Rocky Mountain Front Range, a majority of stations (65) receive too few days per year to make meaningful assessments.

Figure 8. Significance map of trend in number of days with 2.00" of precipitation or greater during the 1949-2013 time period.

Days per year with at least 3.00"

At the 3.00" threshold, only 61 stations had the requisite number of days per year (0.5) of events to qualify for significance analysis. The stations that met the minimum requirements run from eastern Texas northward to the Oklahoma-Kansas boundary and then northeastward to southern New England. of the 61 stations, 43 were in one of the increase categories and only 18 were in a decrease category.

Figure 9. Significance map of trend in number of days with 3.00" of precipitation or greater during the 1949-2013 time period.

All Stations Averaged Together

The primary purpose of this analysis was to assess changes in the frequency of heavy precipitation events in small geographical units. That being said, it is helpful to look at the results when all stations are averaged together. To do this, every station had an average value computed representing the average number of days with at least a certain amount of precipitation (e.g., >=0.05"). Then the value for each year was compared against that average and a percentage above or below the average value was recorded. If, for example, a station averaged 80 days per year with at least 0.05" of precipitation, a year with 88 days would be recorded as 110% of the average. This averaging technique was performed for all station, in all years, for each precipitation threshold. Using percentages prevents stations with large numbers of precipitation days (e.g., New Orleans) from overwhelming stations with small numbers of days (e.g., Las Vegas). Figure 10 shows the number of days per year with at least 1.00" of precipitation between 1949 and 2013 as an example of the spatial variability in heavy rainfall events.

Figure 10. Average number of days with 1.00" of precipitation or greater during the 1949-2013 time period.

As you can see, stations in the southeastern corner of the U.S. have far more days per year with at least 1.00" of precipitation. If, for example, the number of days in Mobile, AL, and Salt Lake City, UT, both increased by 2 days per year, using raw numbers masks the change in Salt Lake City whereas using percentages does not. Therefore, any methodology that does not normalize the data runs the risk of being a de facto analysis of only those stations that have large average annual precipitation amounts.

Days per year with at least 0.05"

The change in the number of days per year with at least 0.05" is pretty chaotic across the entire U.S. There are long periods with consistently upward or downward trends but overall the values are pretty flat. Beginning in 1998, the rate of change dropped noticeably. This also corresponds to period of record or near record worldwide temperatures. The p-value of 0.55 indicates that the overall trend is not statistically significant.

Figure 11. Annual average of each station's percentage from the long-term average number of days with at least 0.05" of precipitation.

Days per year with at least 0.50"

The nationwide change in the number of days per year with at least 0.50" consistently increased for most of the 65 year analysis period. As with the 0.05" chart, the rate of change dropped in 1998. The p-value of 0.004 indicates that the trend is strongly statistically significant at the 95% (and even at the 99%) level over the course of the analysis period.

Figure 12. Annual average of each station's percentage from the long-term average number of days with at least 0.50" of precipitation.

Days per year with at least 1.00"

The change in the number of days per year with at least 1.00" was strongly positive. The increase in the number of days per year is greater than 10% and the post-1998 deviations from the prior two charts are just 1 or 2 year anomalies on the 1.00" chart. In fact, the p-value of 0.0001 indicates that the trend is statistically significant at the 99% level. All 207 stations were included in this analysis – even the 17 stations that do not average at least 0.5 days per year with 1.00" of precipitation.

Figure 13. Annual average of each station's percentage from the long-term average number of days with at least 1.00" of precipitation.

Days per year with at least 2.00"

The change in the number of days per year with at least 2.00" was even more strongly positive. The p-value of 0.0008 indicates a very high degree of statistical significance. Since the vast majority of the excluded stations are in the western U.S., this chart essentially reflects the statistical trend of the eastern half of the U.S. only. As Figures 5 and 6 demonstrate, the trends for the eastern half of the U.S. is much more prominent than for the western half. All stations were included in this analysis.

Figure 14. Annual average of each station's percentage from the long-term average number of days with at least 2.00" of precipitation.

Days per year with at least 3.00"

The change in the number of days per year with at least 3.00" was strongly positive, but less than the 2.00" threshold. The p-value of 0.0001 indicates a very high degree of statistical significance. The map for 3.00" trends appears chaotic when looking at individual stations but when aggregated, the trend is unmistakably upward. However, since the stations that experience 3.00" precipitation events are geographically concentrated in approximately 1/4 of the U.S., this should be considered a regional trend.

Figure 15. Annual average of each station's percentage from the long-term average number of days with at least 3.00" of precipitation.

Conclusion

We showed that the rate of small precipitation events has not changed much in the last 64 years (see Figure 5). However, when the precipitation intensity rises, the strength of the statistical significance rises. Most of the eastern half of the U.S. has experienced an increase in the number of days with at least 0.50", 1.00", 2.00", and 3.00" of precipitation. The western half of the country has, on average, seen a slight decline in the rate of those precipitation thresholds when enough observations are available for analysis– but not at a statistically significant level.

At the station level, the long-term trend of days at different intensity thresholds tells a more complete story than just looking at regional data using state boundaries. While data at an individual station is not sufficient to draw very many conclusions, aggregating station data in this manner allows us to draw new conclusions about how precipitation patterns change over space and time.

A complete list of the stations used in the analysis can be found Here.