Abstract
Precipitation is an essential climate variable and a fundamental part of the
global water cycle. Given its importance to society, precipitation is often
assessed in climate monitoring activities, such as in those led by the Coperni-
cus Climate Change Service (C3S). To undertake these activities, C3S predomi-
nantly uses ERA5 reanalysis precipitation. Research has shown that short-
range forecasts for precipitation made from this reanalysis can provide valu-
able estimates of the actual (observed) precipitation in extratropical regions
but can be less useful in the tropics. While some of these limitations will be
reduced with future reanalyses because of the latest advancements, there is
potentially a more immediate way to improve the precipitation estimate.
This is to use the precipitation modelled in the Four-Dimensional Variational
(4D-Var) data assimilation window of the reanalysis, and it is the aim of this
study to evaluate this approach. Using observed 24-h precipitation accumula-
tions at 5637 stations from 2001 to 2020, results show that smaller root-
mean-square errors (RMSEs) and mean absolute errors are generally found
by using the ERA5 4D-Var precipitation. For example, for all available days
from 2001 to 2020, 87.5% of stations have smaller RMSEs. These improvements
are driven by reduced random errors in the 4D-Var precipitation because it is
better constrained by observations, which are themselves sensitive to or
influence precipitation. However, there are regions (e.g., Europe) where larger
biases occur, and via the decomposition of the Stable Equitable Error in
Probability Space score, this is shown to be because the 4D-Var precipitation
has a wetter bias on ‘dry’ days than the standard ERA5 short-range forecasts.
The findings also highlight that the 4D-Var precipitation does improve the
discrimination of ‘heavy’ observed events. In conclusion, an improved ERA5
precipitation estimate is largely obtainable, and these results could prove
useful for C3S activities and for future reanalyses, including ERA6
global water cycle. Given its importance to society, precipitation is often
assessed in climate monitoring activities, such as in those led by the Coperni-
cus Climate Change Service (C3S). To undertake these activities, C3S predomi-
nantly uses ERA5 reanalysis precipitation. Research has shown that short-
range forecasts for precipitation made from this reanalysis can provide valu-
able estimates of the actual (observed) precipitation in extratropical regions
but can be less useful in the tropics. While some of these limitations will be
reduced with future reanalyses because of the latest advancements, there is
potentially a more immediate way to improve the precipitation estimate.
This is to use the precipitation modelled in the Four-Dimensional Variational
(4D-Var) data assimilation window of the reanalysis, and it is the aim of this
study to evaluate this approach. Using observed 24-h precipitation accumula-
tions at 5637 stations from 2001 to 2020, results show that smaller root-
mean-square errors (RMSEs) and mean absolute errors are generally found
by using the ERA5 4D-Var precipitation. For example, for all available days
from 2001 to 2020, 87.5% of stations have smaller RMSEs. These improvements
are driven by reduced random errors in the 4D-Var precipitation because it is
better constrained by observations, which are themselves sensitive to or
influence precipitation. However, there are regions (e.g., Europe) where larger
biases occur, and via the decomposition of the Stable Equitable Error in
Probability Space score, this is shown to be because the 4D-Var precipitation
has a wetter bias on ‘dry’ days than the standard ERA5 short-range forecasts.
The findings also highlight that the 4D-Var precipitation does improve the
discrimination of ‘heavy’ observed events. In conclusion, an improved ERA5
precipitation estimate is largely obtainable, and these results could prove
useful for C3S activities and for future reanalyses, including ERA6
| Original language | English |
|---|---|
| Number of pages | 8 |
| Journal | Atmospheric Science Letters |
| DOIs | |
| Publication status | Published - 4 Dec 2023 |
Keywords
- ERA5s
- observations
- precipitation evaluation
