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the LPM provides a much more accurate way of quantifying dry days. d. Analyzing the effects of sensor type on subdaily variability of rainfall We analyzed the effect of high-resolution monitoring on rainfall intensities, duration of rainfall events, and drizzle, that is, liquid precipitation with drops of smaller size and falling speed than those of rain, as defined by the LPM manual and by Gunn and Kinzer (1949) . Analyses for both intensity and duration were based on calculated maximums
the LPM provides a much more accurate way of quantifying dry days. d. Analyzing the effects of sensor type on subdaily variability of rainfall We analyzed the effect of high-resolution monitoring on rainfall intensities, duration of rainfall events, and drizzle, that is, liquid precipitation with drops of smaller size and falling speed than those of rain, as defined by the LPM manual and by Gunn and Kinzer (1949) . Analyses for both intensity and duration were based on calculated maximums
PTs (drizzle, rain, mixed rain/snow, snow grains, snow aggregates, hail). Nonhydrometeors (insects, debris) can appear as precipitation and the housing of the instrument is a surface on which precipitation can rebound into the beam. Partial beam hits are accounted for with internal processing. 2) Campbell Scientific PWS100 The Campbell Scientific Present Weather Sensor 100 (PWS100) uses a forward-scattering technique using four light beams and two receiving diodes: one diode at a vertical angle
PTs (drizzle, rain, mixed rain/snow, snow grains, snow aggregates, hail). Nonhydrometeors (insects, debris) can appear as precipitation and the housing of the instrument is a surface on which precipitation can rebound into the beam. Partial beam hits are accounted for with internal processing. 2) Campbell Scientific PWS100 The Campbell Scientific Present Weather Sensor 100 (PWS100) uses a forward-scattering technique using four light beams and two receiving diodes: one diode at a vertical angle
are the only cloud type that cover over 25% of the world’s oceans. It is estimated ( Ramanathan et al. 1989 ) that an increase of a few percent of cloud cover, or a comparable increase in stratocumulus cloud albedo, would counter the anticipated greenhouse warming of the next century, while similar decreases would double the warming. Because of the large spatial coverage and persistence of marine stratocumulus, drizzle exerts a powerful influence on the structure and longevity of stratocumulus
are the only cloud type that cover over 25% of the world’s oceans. It is estimated ( Ramanathan et al. 1989 ) that an increase of a few percent of cloud cover, or a comparable increase in stratocumulus cloud albedo, would counter the anticipated greenhouse warming of the next century, while similar decreases would double the warming. Because of the large spatial coverage and persistence of marine stratocumulus, drizzle exerts a powerful influence on the structure and longevity of stratocumulus
oceans. Due to the significant limitation of in situ observations over ocean, satellites remain the main source for observation-based precipitation estimates there. Behrangi et al. (2014) developed a Merged CloudSat , Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) precipitation product (MCTA) that considers the entire precipitation histogram from drizzle and snowfall (from CloudSat ) to intense
oceans. Due to the significant limitation of in situ observations over ocean, satellites remain the main source for observation-based precipitation estimates there. Behrangi et al. (2014) developed a Merged CloudSat , Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) precipitation product (MCTA) that considers the entire precipitation histogram from drizzle and snowfall (from CloudSat ) to intense
somewhat arbitrary, it is widely employed within the hydrometeorology community for climate change-related studies ( McErlich et al. 2023 ). The usage of lower thresholds based on measurable precipitation (e.g., 0.1 mm or 0.1 in.) would result in an overestimated frequency of WD in the RCMs (compared to E-OBS) due to a “drizzle effect” (too frequent light rain), which is commonly present in climate models ( Chen et al. 2021 ). Some studies suggest higher WD thresholds for wetter regions ( Fdez
somewhat arbitrary, it is widely employed within the hydrometeorology community for climate change-related studies ( McErlich et al. 2023 ). The usage of lower thresholds based on measurable precipitation (e.g., 0.1 mm or 0.1 in.) would result in an overestimated frequency of WD in the RCMs (compared to E-OBS) due to a “drizzle effect” (too frequent light rain), which is commonly present in climate models ( Chen et al. 2021 ). Some studies suggest higher WD thresholds for wetter regions ( Fdez
–coalescence processes alone do not change the precipitation flux within a column, the retrieved rain rate still increases toward the surface ( Fig. 4e ). By examining the contributions of drizzle ( D < 0.5 mm) and raindrops (2 mm < D < 4 mm) to the DSDs, the former shows a secondary peak at 1 km height followed by a rapid decrease downward ( Fig. 5a ), while the number of big raindrops constantly increases toward the ground below the ML ( Fig. 5b ). The increase of rain rate toward the surface can be explained
–coalescence processes alone do not change the precipitation flux within a column, the retrieved rain rate still increases toward the surface ( Fig. 4e ). By examining the contributions of drizzle ( D < 0.5 mm) and raindrops (2 mm < D < 4 mm) to the DSDs, the former shows a secondary peak at 1 km height followed by a rapid decrease downward ( Fig. 5a ), while the number of big raindrops constantly increases toward the ground below the ML ( Fig. 5b ). The increase of rain rate toward the surface can be explained
already demonstrated in Dolant et al. (2018a) and Langlois et al. (2017) . The method does yield an number of omissions and commissions. Events other than ROS can cause liquid on the surface of the snowpack and trigger the algorithm (e.g., melt), or some ROS events might be too light to be detected (e.g., light drizzle). The threshold can be adjusted based on the study objectives. The biases of the data are unknown and remain a source of uncertainty in this study. The rasters were produced at a
already demonstrated in Dolant et al. (2018a) and Langlois et al. (2017) . The method does yield an number of omissions and commissions. Events other than ROS can cause liquid on the surface of the snowpack and trigger the algorithm (e.g., melt), or some ROS events might be too light to be detected (e.g., light drizzle). The threshold can be adjusted based on the study objectives. The biases of the data are unknown and remain a source of uncertainty in this study. The rasters were produced at a
and 13d indicate how precipitation relates to MADV and CAPE. Large precipitation (large dot) is more likely triggered when MADV is more negative, and drizzle is usually generated under less negative MADV or even positive MADV. Compared with natural experiments (i.e., green dots), more precipitation occurs in the irrigation experiment (i.e., orange dots), especially in the red box where MADV is small and CAPE is large. These newly added dots are dense but generally small, which is consistent with
and 13d indicate how precipitation relates to MADV and CAPE. Large precipitation (large dot) is more likely triggered when MADV is more negative, and drizzle is usually generated under less negative MADV or even positive MADV. Compared with natural experiments (i.e., green dots), more precipitation occurs in the irrigation experiment (i.e., orange dots), especially in the red box where MADV is small and CAPE is large. These newly added dots are dense but generally small, which is consistent with
1994 ; Maurer et al. 2010 ; Abatzoglou and Brown 2012 ). However, the weighted average approach leads to a reduction in extremes for all variables and an increase in drizzle days when downscaling precipitation ( Pierce et al. 2014 ). Additionally, this approach does not scale well with domain size, since a sufficiently large domain (e.g., the CONUS) includes locations separated by enough distance to have weather that is uncorrelated, making it harder to find observed days that happen to match the
1994 ; Maurer et al. 2010 ; Abatzoglou and Brown 2012 ). However, the weighted average approach leads to a reduction in extremes for all variables and an increase in drizzle days when downscaling precipitation ( Pierce et al. 2014 ). Additionally, this approach does not scale well with domain size, since a sufficiently large domain (e.g., the CONUS) includes locations separated by enough distance to have weather that is uncorrelated, making it harder to find observed days that happen to match the
imagers and sounders. The V07 PMW products are based on GPROF V07 ( Kummerow 2022b ), also known as GPROF 2021. In GPROF V07, the a priori databases were constructed from the GPM radar–radiometer combined algorithm (V07), CONUS Multi-Radar Multi-Sensor System (MRMS) precipitation over snow surface types, and ERA5 precipitation over sea ice surface types. There are few changes in the V07 algorithm compared to the previous version (V05), including adding drizzle and light precipitation (up to 0.2 mm h
imagers and sounders. The V07 PMW products are based on GPROF V07 ( Kummerow 2022b ), also known as GPROF 2021. In GPROF V07, the a priori databases were constructed from the GPM radar–radiometer combined algorithm (V07), CONUS Multi-Radar Multi-Sensor System (MRMS) precipitation over snow surface types, and ERA5 precipitation over sea ice surface types. There are few changes in the V07 algorithm compared to the previous version (V05), including adding drizzle and light precipitation (up to 0.2 mm h
