We download the latest available MSWX data. We always use MSWX-Past data when available and we complete for very recent data with the MSWX-NRT (MSWX near real-time) extension. 

The object studied is a surface-pressure pattern over a certain region and averaged over a certain number of days, that has lead to a extreme weather conditions.

More specifically, after obtaining the data, we define the length of the event in terms of days when the largest impacts from extreme weather conditions (heat, wind, rain) have been reported and we select the geographical region to analyse based on the meteorological phenomena that caused the event. For example, in the case of a summer heatwave we would select a region including the high pressure causing the heatwave. The length of the event is specified in the title of the Climameter the region is exactly the one shown in the map.  

We download surface pressure, near-surface temperature, total precipitation and wind-speed data from MSWX with a daily time resolution.  In order to account for the seasonal cycle in surface pressure and temperature data, we remove at each grid point and for each day the average of the pressure and temperature values for all the corresponding calendar days.
For surface pressure this removes the effect of varying surface elevation in space. Total precipitation and wind-speed data are not preprocessed.  If the duration of the event is greater than one day, we perform a moving average of the length of the event duration on all datasets.
Note that there can be local discrepancies between local station observations and the gridded product that is used in ClimaMeter.

The search of similar past events is based on defining analogues of the identified surface pressure patterns over the chosen spatio-temporal domain (see FAQ 2).  We split the dataset 1979-Present in two parts {of equal length} and consider the first half of the satellite era  as "past" and the second part as "present" separately. We use data from MSWX. We consider the first period as representative of a past world with a weaker anthropogenic influence on climate than the second period, which represents the present world affected by anthropogenic climate change.  The analogues search is only performed on surface pressure data. Results reported for temperature, precipitation and wind-speed data are always associated with surface pressure analogues.

For each period, we examine all daily surface pressure data and select the best 15 analogues (*), i.e. the data minimizing the Euclidean distance to the event itself. The number of 15 corresponds approximately to the smallest 1‰ Euclidean distances in each subset of our data. We tested the extraction of 10 to 20 analogues, without finding qualitatively important differences in our results. For the present period, as is customary in attribution studies, the event itself is excluded. In addition, we do not search for analogues within a window of 21 days centered on the date of the event (central date for events that last for longer than one day).

(*) For longer events such as floods occurring on an entire season, we may use a larger sample of analogues. If this is the case, this will be specified in the event description.

Here, we assume that over 20 years is a long enough period to average out high-frequency interannual climate variability. To account for the possible influence of low-frequency modes of natural variability in explaining the differences between the two periods, we also consider the possible roles of the El Niño-Southern Oscillation (ENSO), the Atlantic Multidecadal Oscillation (AMO), and the Pacific Decadal Oscillation (PDO). We perform this analysis using monthly indices produced by NOAA/ERSSTv5. Data for ENSO and AMO are retrieved from the Royal Netherlands Meteorological Institute (KNMI) Climate Explorer, while the PDO time series is downloaded from the NOAA National Centers for Environmental Information (NCEI). 

The gauge can take values between 0% (pointing to the left) and 100% (pointing to the right). We look on whether the analogues occurred in a statistically significant different phase of  El Niño-Southern Oscillation (ENSO), the Atlantic Multidecadal Oscillation (AMO), and/or the Pacific Decadal Oscillation (PDO). Whenever a statistically significant differences between phases of  ENSO, AMO or PDO is observed we subtract 30% from the gauge value starting from 95%.  We do not use 0% or 100% to acknowledge data and analysis uncertainties.

The gauge can take values between 0% (pointing to the left) and 100% (pointing to the right). We define several quantities to support our interpretation of analogue-based assignment, including the analogue quality Q, which is the average Euclidean distance of a given day from its closest analogues. If for both the periods, the value of Q for the event is below the 75th percentile of the distribution of Qs computed for all days in each period, we assign the gauge the value 5% (similar events have occurred in the past). If instead, for both periods the value of Q is below the 95th percentile, we assign 30%. If for one of the two periods this condition does not hold, we assign 60%. Finally, if the value of Q for the extreme event exceeds the 95th percentile for both periods, we assign the gauge the value 95% (the event is unique).  We do not use 0% or 100% to acknowledge data and analysis uncertainties.

For pressure, we display the difference between the average surface pressure in the region for the duration of the event minus the average surface pressure for those same calendar days in the whole period 1979 to present. For example, if an extreme event happens on the 20th-27th July 2023, the pressure anomaly at a given location is the average pressure on the 20th to 27th July 2023 minus the average pressure on all 20th to 27th Julys from 1979 to 2023. For temperature, we do as for pressure. We do not compute anomalies for precipitation and windspeed because of their very noisy nature.

The pressure map displays the difference in the average pressure for all analogues in the present period minus the average pressure for all analogues in the past period. The same is done for temperature. To determine significant changes between the two periods, we adopt a bootstrap procedure which consists of pooling the dates from the two periods together, randomly extracting 15 dates from this pool 100 times, creating the corresponding difference maps and marking as significant only grid point changes more than two standard deviations above or below the mean of the bootstrap sample.

Significance of the changes between the distributions of variables during the past and present periods is evaluated using a two-tailed Cramér-von Mises test at the 0.05 significance level. If the p-value is smaller than 0.05, the null hypothesis that both samples are from the same distribution is rejected, namely we interpret the distributions as being significantly different. We use this test to determine the role of natural variability.

We use preliminary MSWX data to release our report as soon as possible after an extreme event has occurred. When updated climate data is available, we may update our report. We also welcome feedback from researchers and the general public, and may update our reports as a result of this feedback. This means that we may update a given report multiple times. For every report, we provide under the title the first publication date, the date of the latest update, and the date when the report was finalised. Once the report is finalised, we will not change it further, and we will provide a PDF version of the report as well as a DOI to cite the report. The finalisation of the report may happen several months after the occurrence of the extreme event being analysed.