Climate change is expected to change precipitation characteristics and particularly the frequency and magnitude of precipitation extremes. Satellite observations form an important part of the observing system necessary to monitor both temporal and spatial patterns of precipitation variability and extremes. As satellite-based precipitation estimates are generally only indirect, however, their reliability has to be verified.
This study evaluates the ability of the satellite-based
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The Clausius-Clapeyron relationship suggests that the observed warming of the troposphere in response to increasing greenhouse gas concentrations intensifies the hydrological cycle (Trenberth 1999). According to Trenberth et al. (2003) and Allan and Soden (2008) precipitation will not only increase on average, but more importantly also in intensity, leading to enhanced con- tributions of heavy and extreme events to total precip- itation. Such a change will have large impacts on many societal areas such as water resource management, ag- riculture, and infrastructure planning, for example, for mitigating flood risk. Owing to the stochastic nature of precipitation long-term observations with high temporal and spatial resolution are required to document, analyze, and improve our understanding of past precipitation var- iability and changes. This in turn will help to improve model predictions of future precipitation variability and extremes that form the basis for the development of strategies of adaptation and mitigation.
Precipitation estimates over land areas were typically derived from surface rain gauge observations at auto- mated or human-operated sites. The main advantage of these data is their long-term temporal coverage (e.g., Brienen et al. 2013). In most parts of the world they extend back to the early decades of the twentieth cen- tury or even earlier. However, surface rain gauge ob- servations are very inhomogeneously distributed in space and often suffer from a large fraction of missing data resulting in inadequate temporal and spatial sam- pling even over relatively densely sampled areas like
Satellite-based precipitation estimates, which are avail- able since the late 1970s, provide spatially homogeneous observations with almost global coverage. These esti- mates are, however, indirect because they rely on the interpretation of emitted or scattered radiation received by the satellite instruments. Retrieval algorithms can be categorized according to the type of radiation exploited (Kidd and Levizzani 2011): 1) scattered solar [visible (VIS) and near infrared (NIR)] and emitted infrared (IR) radiation, 2) emitted microwave radiation [passive microwave (PMW)] and backscattered radar-emitted microwave radiation [active microwave (AMW)], and 3) multisensor methods using a mixture of 1 and 2. VIS-, NIR-, and/or IR-based algorithms use information on cloud properties as proxy for rainfall, taking advantage of the relationship between rainfall and the visible brightness of clouds, the cloud microphysical properties derived from VIS/NIR, and IR observations (e.g., Lensky and Rosenfeld 2006; Roebeling and Holleman 2009), or the cloud-top temperature obtained from IR observa- tions [e.g., Geostationary Operational Environmental Satellite Precipitation Index (GPI) by Arkin and Meisner (1987)]. VIS-/NIR- and/or IR-based retrievals are par- ticularly utilized for observations from geostationary orbit [e.g., Meteosat and Geostationary Operational Environmental Satellite (GOES)] and the data products therefore benefit from the high temporal resolution.
The PMW techniques have the closest relation to precipitation processes and can be further divided into emission- (used over ocean) and scattering-based (used over land) algorithms depending on the processes ex- ploited for the retrievals. The main challenges for PMW algorithms are the detection and quantification of precipitation over land, especially over cold, snow, ice, and desert surfaces and the detection of light rain (Ferraro et al. 1998). The relatively long wavelengths still prohibit, however, the deployment of microwave sensors on geostationary orbits, thus instruments are only found on board low-earth-orbiting (LEO) satellites [e.g., Special Sensor Microwave Imager (SSM/I); Ad- vanced Microwave Scanning Radiometer (AMSR-E)], which allow only low temporal resolutions in the range of hours. Examples of algorithms include the SSM/I operational precipitation rate algorithm from Ferraro (1997) and the Bayesian Rain Algorithm including Neural Networks (BRAIN) algorithm from Viltard et al. (2006).
AMW instruments provide the most direct informa- tion on precipitation from satellites. So far, the
Other multisensor methods exploit synergies of geo- stationary VIS/IR and PMW algorithms to generate estimates with higher temporal and spatial resolution than possible with single instruments [in addition to the algorithms mentioned above, the Climate Prediction Center morphing technique (CMORPH) (Joyce et al. 2004) and
The considerable efforts in algorithm development and the exploitation of sensor synergy has led in the meantime to datasets that are relatively long (covering at least one decade) and mature enough to be used for analyses of the interannual variability of precipitation and even provide the required spatial and temporal resolution to effectively capture precipitation extremes, which is the focus of this study.
To estimate the value of satellite datasets for climato- logical studies, it is crucial to evaluate the results against high-quality ground-based observations. Such an eval- uation is necessary to reveal both the strengths and weaknesses of the satellite-based estimates and might even lead to further improvements of retrieval algo- rithms and satellite sensors. This is particularly impor- tant assuming that the role of satellite measurements in observing precipitation, which form part of the essential climate variables (ECV) listed by the Global Climate Observation System (GCOS), will increase in the future. Many validation studies for satellite-based precipitation estimates have been carried out using ground-based observations, for example in the framework of the In- ternational
The aim of this study is to assess the ability of the GPCP1DD rainfall estimates (Huffman et al. 2001) over
The paper is divided into five sections. The GPCP1DD and E-OBS datasets are described in section 2, section 3 gives details on the analysis methodology applied, and in section 4 the comparison results are presented and dis- cussed. A summary is given in section 5 together with some concluding remarks.
Between 408N and 408S, the GPCP1DD algorithm uses SSM/I precipitation estimates together with geo- synchronous IR (GEO-IR) and low-earth-orbit IR (LEO- IR) brightness temperatures to derive the so-called threshold-matched precipitation index (TMPI). The TMPI is an adaptation of the GOES Precipitation Index (GPI) (Arkin and Meisner 1987), which assigns a con- stant conditional rain rate of 3 mm h21 to all pixels that have temperature values lower than 235 K and zero rain rate to all others. For the TMPI the IR temperature threshold is set locally by month using the SSM/I-based precipitation frequency and a single (local) conditional rain rate based on the monthly GPCP SG product (Huffman et al. 2001). The latter is generated by first using the higher accuracy of low-orbit PMW observa- tions to calibrate the more frequent GEO-IR observa- tions and then adjusting in a second step the resulting combined satellite-based product using the Global Pre- cipitation Climatology Centre (GPCC) rain gauge analysis.
Outside the latitudinal band from 408Nto408S-and thus over the main part of the area of interest of this study-precipitation estimates are computed based on recalibrated Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) and Atmospheric Infrared Sounder (AIRS) data from polar- orbiting satellites. TOVS data is used for the time span up to
Direct comparison of precipitation products provid- ing area averages such as satellite-based products to rain gauge data representing point observations in the context of extreme events is problematic. In our study the GPCP1DD precipitation statistics are there- fore validated with the E-OBS dataset provided by the
The E-OBS dataset is based on daily observations from approximately 4500 land stations (see Fig. 1). Ac- cording to the authors, the dataset provides a best esti- mate of gridbox averages rather than point values in order to enable direct comparisons with products pro- viding real areal averages such as regional climate model output or satellite-derived datasets. For precipitation, the interpolation applied in E-OBS comprises three steps (Haylock et al. 2008): 1) interpolation of the monthly totals using three-dimensional thin-plate splines, 2) interpolation of the daily anomalies using indicator and universal kriging, and 3) a combination of steps 2 and 3. As an interpolation uncertainty, daily standard errors are provided for every grid. It is important to keep in mind that at gauge stations daily accumulations of precipitation are mostly reported from 0900 to
Uncertainties of the E-OBS dataset are mainly asso- ciated with three major problems. First is the inaccuracy of daily station data due to instrumental errors and er- rors associated with observational practices. The latter also includes the underreporting of daily precipitation owing to spurious zeros and incomplete records, which may result in a negative bias if not removed. Second is the inhomogeneity of the station distribution, and third, the temporally varying number of gaps in the observa- tional data. The first problem is tackled within the gen- eration of the E-OBS dataset by applying a series of quality checks on the raw station observations (Haylock et al. 2008). This partly reduces these errors but does not remove them completely. Thus, this uncertainty pro- pagates to the E-OBS grids. The two latter problems additionally affect the gridbox area-average estimates in three main ways (Haylock et al. 2008;Hofstra et al. 2010): representativeness (estimate will not be a ''true'' areal average), smoothing (contributions by using stations out- side the grid box for gridbox estimates), and variable de- gree of smoothing depending on station density across the grid domain. The representation of extremes is in partic- ular influenced by (over and under) smoothing; therefore,
c. Data processing
In a first step both datasets-GPCP1DD and E-OBS- were brought to the same resolution. To this end the daily precipitation sums of E-OBS were averaged onto the 183 18 grid, that is, the grid at which the GPCP1DD dataset is provided. Furthermore, both datasets were confined to the region of interest extending over 358-708N, 108W-408E that encompasses several climate zones ranging from maritime to continental and semiarid to temperate.
To assure a comparable quality level (with respect to representativeness and minimization of the smoothing effect) between E-OBS grid values and thereby fol- lowing the recommendations of
a. General precipitation evaluation
For the evaluation of precipitation statistics from E-OBS and GPCP1DD we used the threshold of 1 mm day2 1 to distinguish between wet and dry days; that is, wet days are defined as those days with pre- cipitation totals
* MEAN (mm day 2 1): average over all days,
* INT (mm day 2 1): average over all wet days,
* NWET: number of wet days,
* Q90, Q95, and so on (mm day 2 1): 90th, 95th, and so on, percentile estimated from the empirical (wet day) distribution functions,
* bias ratio (2): the ratio of average GPCP1DD to average E-OBS,
* quantile-quantile plots (QQ plots; see Wilks 2006),
* cumulative frequency distributions of daily precipitation.
The three first diagnostics provide standard measures of precipitation. Empirically estimated percentiles are used to quantify the skill of representing precipitation extremes. The bias ratio is used to quantitatively com- pare the results of the two datasets. The QQ plots provide information on the empirical quantiles of the E-OBS- and GPCP1DD-based wet-day time series. They serve to assess the consistency between the ground- and satellite-based distributions of precipitation. Finally, we used the nonparametric Kolmogorov-Smirnov test (KS test; see Wilks 2006) in order to further investigate and quantify the comparability of the statistical structure of the two daily precipitation datasets.
All results have been computed at each grid box so as to avoid the pooling of data over areas belonging to different climate zones. Additionally, results are pre- sented separately per season to account for the changing precipitation type and weather regimes, which influence the performance of the satellite-based estimates but also the uncertainty and representativeness of the in situ measurements. The seasons are defined as winter: De- cember to February (DJF), spring: March to May (MAM), summer: June to August (JJA), and autumn: September to November (SON).
b. Extreme precipitation assessment
Extreme precipitation events are defined as daily to- tals exceeding high (e.g., 90th and 95th) percentiles. These percentile thresholds were calculated from the sample of all wet days in the analyzed time period (i.e., 1998 to 2008). Percentiles were chosen instead of abso- lute thresholds because they are more easily comparable between different climate regions (see Groisman et al. 2005). We used two approaches, namely deterministic (point by point) and fuzzy verification to assess how well extreme events are represented by the GPCP1DD da- taset compared to station data.
1) DETERMINISTIC APPROACH
Traditionally, gridded precipitation products are com- pared using deterministic verification methods, which are based on simple (spatial and temporal) point-by-point matching. To assess the agreement between the occur- rence of extreme events seen by the satellite dataset on the one hand and the ground-based dataset on the other, the respective rain rates are transformed to binary (yes- no) indicators of extreme events (using a given extreme threshold). These matched indicator grid pairs are then counted to complete the standard contingency table (Wilks 2006) from which common evaluation measures like frequency bias, the probability of detection (POD), false alarm rate (FAR), threat score (TS), and equitable threat score (ETS) can be estimated (e.g., Wilks 2006). We use quantile-specific POD and quantile-specific FAR as proposed by AghaKouchak (2011) for the de- terministic verification. The quantile-specific POD and FAR values were calculated based on the time series of the E-OBS and GPCP1DD for each grid box as follows:
* The quantile probability of detection (QPOD) is de- fined as the POD above a certain percentile (or quantile) threshold (here, e.g., Q90 and Q95 repre- senting the 90th and 95th percentile, respectively) and is equal to the ratio of the number of precipitation events being correctly detected as exceeding a given threshold to the total number of precipitation occur- rences above the same threshold in the reference. QPOD ranges between 0 and 1, with 1 indicating the perfect score. Thresholds are calculated separately for each of the datasets.
* The quantile false alarm rate (QFAR) is defined as the FAR above a certain percentile (or quantile) thresh- old. QFAR is equal to the ratio of the number of precipitation events being falsely indicated as exceed- ing a given threshold to the total number of correct and false occurrences over the same threshold as in- dicated by the reference. QFAR ranges between 0 and 1, with 0 indicating the perfect score. As for QPOD, thresholds are calculated separately for each of the datasets.
2) FUZZY APPROACH
Fuzzy verification or neighborhood methods as de- scribed in Ebert (2008) aim at relaxing the requirement for exact matching by allowing slight displacements. The maximum displacement allowed is defined by a local neighborhood (or window) around the grid box of in- terest. In this study, both spatial and temporal dis- placements are considered. Following this approach a spatiotemporal neighborhood of grid boxes is defined around each central grid box. For example, for a given spatiotemporal scale of 58 and 3 days the neighborhood encompasses 5 3 5 3 3 5 75 grid boxes. The treatment of the neighborhood data depends on the selected fuzzy method and includes, for example, averaging, thresholding, or the generation of empirical frequency distributions.
From the available fuzzy methods we chose the fractions skill score (FSS) (see also Roberts and Lean 2008). This score directly compares the fractional coverage of events (here extreme events as defined above) in the given spa- tiotemporal neighborhood defined as the ratio of the number of grid boxes in the spatiotemporal neighborhood where the extreme event occurs to the total number of valid neighborhood grid boxes. A dataset shows useful skill if the fraction of events, as seen by, for example, the satellite product, is similar to the fraction obtained from the ground-based product. The calculation of the FSS encompasses the following steps. 1) For each selected space-time scale pair (18 and 1 day, 18 and 3 days, 38 and 3 days, etc.) and thresholds (e.g., Q90) the daily pre- cipitation accumulations from GPCP1DD and E-OBS were converted to fractions of extreme events. 2) These fractions were then used to compute a fractions Brier score (FBS), which is defined in Ebert (2008):
where hPx i is the fraction of grid boxes in a neighbor- hood with extreme events observed by GPCP1DD; hPy i is the fraction of the neighborhood with extreme events observed by E-OBS, where his indicate that the frac- tions are calculated based on the neighborhood sur- rounding the grid box of interest for the indicated spatiotemporal scale; N is the number of neighborhoods in the domain considered. In this study, the FBS is cal- culated per grid box so that the FBS is calculated for the temporal domain (i.e., the time period covered). There- fore, N is equal to the total number of days per season for 11 years [e.g., 1012 days (and thus also 1012 neighbor- hoods) for the summer season]. 3) FBS and hPx i and hPy i are then used to calculate the FSS (Ebert 2008):
The FSS ranges between 0 and 1 with 1 indicating the perfect score. The value of FSS above which the assessed dataset is considered to have useful (better than ran- dom) skill is given by
where fy is the domain average fraction observed by the reference dataset (Roberts and Lean 2008); that is, here the average fraction of extreme events observed by E- OBS at a specific grid point over the entire time period.
The values of the extreme thresholds were calculated per grid box at a 18 resolution. With increasing size of the spatial neighborhood, the extreme threshold values were averaged over the spatial neighborhood. With in- creasing (spatial) size the neighborhood window will cross the boundaries of the study area and, therefore, also include no-data values. So, a neighborhood was only considered when at least 50% of the neighborhood grid boxes provided valid values. This leads to a de- crease in the size of the area along the boundaries with increasing spatial scale.
a. Climatological statistics
Based upon 11 years of daily rainfall estimates, cli- matological means of several basic statistics were cal- culated for both the GPCP1DD and E-OBS datasets. In Figs. 2 and 3, regional maps of mean precipitation (MEAN), mean wet-day intensity (INT), mean number of wet days per year (NWET), and the long-term 90th percentile of wet days (Q90) for E-OBS (left) and GPCP1DD (middle) are presented for winter and sum- mer, respectively. Bias ratio maps (GPCP1DD/E-OBS) are shown on the right-hand side. Table 1 lists the cor- responding summery statistics of these four diagnostics (MEAN, INT, NWET, and Q90) for the entire domain during the time period 1998 to 2008. Qualitatively, the spatial patterns compare satisfactorily for all parameters. In general, the typical features of the respective season are captured well. During the winter season, the weather in
Good agreement between E-OBS and GPCP1DD should be expected for mean precipitation since daily es- timates of GPCP1DD are scaled to match the monthly accumulation provided by the GPCP SG product. Over areas with dense gauge networks, such as
Despite consistent spatial patterns, absolute values differ by season and region (see bias ratio maps on the right-hand side of Figs. 2 and 3). Except for NWET in winter, GPCP1DD gives higher values than E-OBS for all parameters and seasons over the full spatial do- main. Table 2 shows that the overestimation of MEAN, INT, and Q90 by GPCP1DD is highest during win- ter with 50% to 60% higher values than E-OBS, whereas the values are only 10% to 20% higher during summer.
In winter, the overestimation of MEAN is largest in the eastern part of the study area. Since both products have similar NWET values, this overestimation translates to higher INT values in GPCP1DD. In the Mediterranean region GPCP1DD overestimates both MEAN and NWET relative to E-OBS, leading to a closer agreement of INT of both products. The differences due to higher GPCP1DD MEAN values can be explained by the latitude-dependent wind-loss correction applied only to the GPCP1DD product. The differences caused by higher NWET values might relate to unaccounted for effects of surface emissivity in the TOVS-AIRS-based retrieval owing to snow/ice and/or vegetation cover changes (Eyre and Menzel 1989). The performance of the TMPI-based product is not affected by cold/snow surface backgrounds because it is only used up to 408N, that is, only in regions where snow and ice do not play a role.
In summer, the overestimation of MEAN is much lower and mainly restricted to the drier southern part (Mediterranean region) with only very few wet days (NWET , 10). The spatial patterns of largest over- estimation of MEAN mirror the pattern of largest un- derestimation by E-OBS compared to GPCC shown in Fig. 4 (right). The dry region also shows an overestimation of 50% to 100% for NWET, which only corresponds to a few days. The feature is visible across the data region boundary (408N) and might be explained by the low temporal resolution of the TOVS-AIRS-based retrieval (thereby missing short-lived events) and by emissivity- related difficulties of both retrievals over dry areas (Eyre and Menzel 1989; Ferraro et al. 1998). Outside the dry region in the south, relative differences lie below 10%.
The results confirm findings of Bolvin et al. (2009), who found that GPCP1DD tends to overestimate mean precipitation in winter for southern
Figure 5 displays the QQ plots for E-OBS and GPCP1DD quantiles based on the respective wet-day time series separately for winter (DJF, left) and summer (JJA, right). GPCP1DD overestimates, in general (most in winter and least in summer), the frequency of rain along the entire distribution, which results in an overall overestimation of MEAN, INT, and Q90 as previously seen from the climatological maps (Figs. 2 and 3).
The consistency between the wet-day distributions on the gridbox level was further investigated and quantified using the KS test applied separately by season and year. Based on the seasonal statistics for each year, the frac- tion of years for which the hypothesis of consistency between the distributions of the two datasets was not rejected at the 0.05% significance level was calculated. Figure 6 shows the regional maps of this fraction of years for the winter and the summer season. In summer, fractions of 0.8 and higher are found throughout the spatial domain (except for some grid boxes mainly lo- cated in the northern part of
b. Detection of extremes
1) DETERMINISTIC APPROACH
First, we present results of point-by-point compari- sons that are traditionally used for the comparison of gridded products. To quantify the ability of GPCP1DD to capture extreme events, scores of QPOD and QFAR were computed for each 18 grid box for individual cal- endar seasons. Figure 7 shows box-and-whisker plots of QPOD (left) and QFAR (right) for thresholds ranging from Q75 to Q99. For all seasons QPOD decreases with increasing threshold from median values around 0.4 for Q75 to 0.2 for Q95. At the same time QFAR increases with increasing threshold (respective median values in- crease from 0.6 to 0.8). For all thresholds the QPOD (QFAR) values are highest (lowest) in autumn. These results confirm the findings of AghaKouchak et al. (2011), who assessed four satellite-retrieved precipitation prod- ucts (including one microwave-only and three multiple- source products) with respect to their ability to detect extreme precipitation over
These poor scores are not too surprising considering that precipitation is highly variable in space and time, requiring a measurement system with both high tem- poral and high spatial resolution. Precipitation products derived from available observing systems fulfill this re- quirement only to a variable degree. Differences between the products considered in this study stem to a large ex- tent from differences in spatial and temporal sampling: gauges provide theoretically good temporal sampling (but may suffer from missing values), while their spatial sampling can be considered rather poor. The TMPI- based part of the GPCP1DD product provides both good spatial and temporal sampling owing to the GEO- IR data being used (though it only covers the latitude band between 408N and 408S). LEO-based single-sensor products like the TOVS-AIRS-based data product (for the years from 1999 onward), however, can only provide two observations per day. This low temporal sampling results in precipitation fields showing a speckled ''salt and pepper'' pattern compared to the smooth spatial patterns of the E-OBS product. This speckled pattern also translates into the extreme precipitation frequency patterns and can result in spatial mismatches of ex- tent and/or location of extreme rain events as seen by E-OBS and GPCP1DD. Besides the differences in temporal and spatial sampling, differences in the gen- eration process of the products, such as the differ- ent definition of a day, are another reason why exact matching-as required by point-by-point verification- results in poor scores. Because of the difference in the day definition, heavy precipitation events occurring be- tween 0000 and
2) FUZZY APPROACH
Fuzzy verification methods avoid the so-called ''dou- ble penalty'' (Ebert 2008), describing the fact that non- detection at a given day and grid box is punished as well as its detection at an earlier/later or close-by grid box- a natural consequence of the exact match imposed by traditional verification methods. A variety of fuzzy methods are available that try to avoid this phenomenon (Ebert 2008). We chose the fraction skill score (FSS), which builds upon the estimates of the original 1-day and 18 resolution of the GPCP1DD dataset. Figure 8 depicts the FSS using the 90th percentile threshold to define extremes events as a function of spatial and temporal neighborhood sizes for the summer season. The [1, 1] space-time scale pair (i.e., the first number within brackets indicates the spatial neighborhood sizes in de- grees, the second one the temporal neighborhood size in days) in the upper left corner corresponds to the tradi- tional point-by-point verification that results in extremely low FSS values. All grid boxes lie well below the indi- vidual threshold of usefulness (FSSuseful), which at this scale generally ranges between 0.51 and 0.52 (figure not shown here). With temporally and spatially increasing neighborhood size, the effect of mismatches due to sam- pling and difference in the day definition is mitigated, and the FSS steadily increases and reaches values well above the local FSSuseful values, assigning GPCP1DD a useful skill at these scales. The FSS increases from 0.3 (area average for the [1,1] scale pair) to 0.8 (area average for the [7, 7] scale pair). From the [3, 5], [5, 3], and [7, 1] space-time scale pairs onward, the GPCP1DD shows useful skill over almost the entire spatial domain (more than 95% of the grid boxes, as indicated by the numbers shown in the upper left corner of the individual scale pair maps in Fig. 8) except for the southern part of the
To demonstrate the assigned skill of the GPCP1DD dataset time series of extreme precipitation frequency (defined with respect to 90th percentile threshold) are exemplarily shown in Fig. 10 for the [3, 7] space-time scale for two locations. The first time series (upper panel) presents the extreme frequency for all summer seasons at the grid box centered at 43.58N, 25.58W(
Within this study we compared daily precipitation estimates from E-OBS and GPCP1DD over
We found good agreement between the E-OBS and the GPCP1DD dataset not only for the climatological statistics analyzed (MEAN, INT, NWET, and Q90) but also with respect to their distributions. The results revealed pronounced seasonal and regional variations in the performance of GPCP1DD. Larger differences were found in winter and dry regions/seasons where MEAN, INT, and Q90 (winter) and MEAN and NWET (dry regions) are generally overestimated by more than 50%. This may, to a large extent, be explained by the prob- lems of the satellite retrievals used within GPCP1DD to correctly detect precipitation areas over cold, snow, and ice surfaces and in dry regions (due to variations in surface emissivity; e.g., Eyre and Menzel 1989; Ferraro et al. 1998) as well as their inability to detect light or small areas of precipitation and short-lived events. In winter the larger differences may also be attributed to the wind-loss correction applied to the GPCP1DD da- taset (but not to the E-OBS dataset), resulting in a higher MEAN value compared to E-OBS. In summer (outside the dry region in southern
Both traditional deterministic and fuzzy verification methods were used to assess the skill of the GPCP1DD dataset to detect extreme precipitation events. Deter- ministic verification methods showed that with increasing threshold seasonal area median QPOD values drop from 0.4-0.5 for the 75th to 0.2-0.3 for the 95th percentile, while at the same time seasonal area median QFAR values increase from 0.5-0.6 to 0.7-0.8, thereby assigning GPCP1DD only poor skill for the detection of extreme events. These results reinforce findings by AghaKouchak et al. (2011).
The fuzzy method approach proved to be better suited for the dataset comparison of such a highly variable parameter as precipitation because it compensates for any slight displacement by taking neighborhoods (de- fined by space-time scale pairs [degree, days]) around the individual grid boxes into account. From the fuzzy methods available (see Ebert 2008), the fraction skill score (FSS) was used and increasing skill was found at larger spatial and temporal scales with area mean FSS values increasing for the summer season from 0.3 for the [1, 1] space-time scale to 0.8 for the [7, 7] space-time scale. GPCP1DD was found to have useful (better than random) skill in detecting extremes over the entire spatial domain (at least 95% of the grid boxes) from [3, 5], [5, 3], and [7, 1] space-time scale pairs onward in summer and autumn, in spring from [5, 5] and [7, 3], and in winter from [3, 7] and [5, 3] onward. On these scales (and higher) the GPCP1DD is able to represent vari- ability of the frequency of extreme events as depicted by E-OBS. This was exemplarily demonstrated with ex- treme frequency time series of E-OBS and GPCP1DD at two locations. The question of which scale to choose from those identified as skillful will finally depend on individual application-specific requirements with respect to temporal or spatial resolution, quality (FSS value), and spatial coverage (which decreases along the area boundaries with increasing spatial neighborhood size).
GPCP1DD has limitations with respect to the type of extremes that are represented, especially over mid and high latitudes where precipitation estimates mostly rely on single-sensor LEO-based satellite observations that provide only low temporal sampling. Therefore, GPCP1DD does not capture small-scale events like thunderstorms, but precipitation extremes occurring in the context of mesoscale convective systems (MCS) in sum- mer and in frontal systems in winter can be captured. For many applications those large events (occurring on large time and space scales) are more important than small-scale short-term events because the impact on people and in- frastructure is higher. For studying extreme events at smaller scales as well as their changes in intensity and frequency, there is a need for products with higher spatial and temporal resolution, both satellite and ground based. Recent efforts exploit regional networks of gauge obser- vations to generate datasets at higher spatial resolutions for
Another limitation affecting the capability of both IR- and PMW-based satellite retrievals in representing extreme precipitation events in general is the saturation of the relationship between precipitation and the satel- lite measured quantities used to derive the precipitation estimates (IR and PWV brightness temperatures, re- spectively). In the case of PMW techniques, emission- based algorithms (used over the ocean) are affected by saturation effects, resulting in a maximum detectable rainfall rate that varies according to the depth of the rain layer (Adler et al. 1991). IR techniques rely on the as- sumption that colder cloud-top temperatures in the IR are always associated with higher rain rates. On the one hand, this causes heavy precipitation events from warm clouds to be missed; on the other, it also imposes a maximum retrievable rain rate by not being able to represent phys- ical processes leading to a further increase in rainfall not relatedtoanincreaseincloud-topheight.Whereasthe GPCP1DD is well affected by saturation effects in the IR (TOVS-AIRS-based product), the PMW saturation ef- fects do not translate into the final TMPA-based pre- cipitation estimates. This is because GEO-IR is calibrated by matching the precipitation frequencies (of IR and PMW); that is, not the rain rates but the precipitation occurrences derived from SSM/I are used.
Further work shall concentrate on 1) applying the methodology introduced over other regions (
Acknowledgments. We acknowledge the use of the E-OBS dataset provided by the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com), and the data providers in the ECA&D project (http://eca. knmi.nl). The GPCP1DD data were provided by the
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Deutscher Wetterdienst, Offenbach,
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Corresponding author address:
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