Precipitation in the Waikato River Catchment

<p>Observational data and numerical models were used to investigate precipitation in and around the Waikato River catchment. This economically important catchment relies on a dependable precipitation supply for agriculture and hydroelectric generation, with stations generally receiving 2,000 +/- 300 mm of precipitation annually. Long-term and inter-annual variability of total and extreme precipitation were examined using up to 100 years of observational data. Precipitation volumes within the catchment were represented by a five-day smoothed, area-averaged time series, and extreme events were defined as exceeding the 95th percentile. Atmospheric circulation oscillations correlated with the frequency of light precipitation but not with the probability of occurrence or with the magnitude of heavy precipitation events. Also no significant linear variations in precipitation (either annual totals or extreme precipitation characteristics) were found over this period, although temperature increased by 1.15+/-0.45'. A total of 63 heavy precipitation events were identified between 1996 and 2001. An analysis of the prevailing synoptic conditions reveal that heavy precipitation was associated with the passage of cold fronts of cyclones with minima at both 500 and 1000 mb heights. Extended periods of enhanced baroclinicity (succession of cyclones) or blocking anticyclones east of New Zealand have led to flooding in the Waikato catchment. Storm tracking showed that 10% of cyclones originating in the Tasman Sea result in heavy precipitation in the catchment. The accuracy and value of the GFS global precipitation forecasts <= 180 hours were investigated. Depending on forecast lag, the global models correctly predicted the presence of precipitation in 70-80% of forecasts, but the magnitude and distribution were often inaccurate. The probability of receiving precipitation is increased when more members of a lagged ensemble predict it. Forecasts with lags shorter than approximately 96 hours were appropriate to use as boundary constraints for mesoscale modelling. The ability and limitations of mesoscale models to simulate the spatial distribution of precipitation were examined through high-resolution WRF simulations of three heavy precipitation events, and ten different model settings were compared for the January 2006 event. The model consistently under-predicted precipitation. The timing and location of convective precipitation, which accounted for 50% of the precipitation during two events, was physically unconstrained but regional totals were comparable to observations. A continuous two-year numerical simulation was run to provide a precipitation climatology for data-sparse areas. The simulation gave good spatial representation of precipitation and other meteorological variables but tended to under estimate the magnitude of heavy precipitation and over-estimate light precipitation.</p>

Precipitation in the Waikato River Catchment

<p>Observational data and numerical models were used to investigate precipitation in and around the Waikato River catchment. This economically important catchment relies on a dependable precipitation supply for agriculture and hydroelectric generation, with stations generally receiving 2,000 +/- 300 mm of precipitation annually. Long-term and inter-annual variability of total and extreme precipitation were examined using up to 100 years of observational data. Precipitation volumes within the catchment were represented by a five-day smoothed, area-averaged time series, and extreme events were defined as exceeding the 95th percentile. Atmospheric circulation oscillations correlated with the frequency of light precipitation but not with the probability of occurrence or with the magnitude of heavy precipitation events. Also no significant linear variations in precipitation (either annual totals or extreme precipitation characteristics) were found over this period, although temperature increased by 1.15+/-0.45'. A total of 63 heavy precipitation events were identified between 1996 and 2001. An analysis of the prevailing synoptic conditions reveal that heavy precipitation was associated with the passage of cold fronts of cyclones with minima at both 500 and 1000 mb heights. Extended periods of enhanced baroclinicity (succession of cyclones) or blocking anticyclones east of New Zealand have led to flooding in the Waikato catchment. Storm tracking showed that 10% of cyclones originating in the Tasman Sea result in heavy precipitation in the catchment. The accuracy and value of the GFS global precipitation forecasts <= 180 hours were investigated. Depending on forecast lag, the global models correctly predicted the presence of precipitation in 70-80% of forecasts, but the magnitude and distribution were often inaccurate. The probability of receiving precipitation is increased when more members of a lagged ensemble predict it. Forecasts with lags shorter than approximately 96 hours were appropriate to use as boundary constraints for mesoscale modelling. The ability and limitations of mesoscale models to simulate the spatial distribution of precipitation were examined through high-resolution WRF simulations of three heavy precipitation events, and ten different model settings were compared for the January 2006 event. The model consistently under-predicted precipitation. The timing and location of convective precipitation, which accounted for 50% of the precipitation during two events, was physically unconstrained but regional totals were comparable to observations. A continuous two-year numerical simulation was run to provide a precipitation climatology for data-sparse areas. The simulation gave good spatial representation of precipitation and other meteorological variables but tended to under estimate the magnitude of heavy precipitation and over-estimate light precipitation.</p>

Estimation of the observation standard deviation error formula thanks to the a posteriori diagnosis

&lt;p&gt;&lt;em&gt;Within the UERRA project, a daily precipitation reanalysis at a 5,5km resolution has been realized from 1961 to 2015. The reanalysis was obtained by the MESCAN analysis system which combines an a priori estimate of the atmosphere &amp;#8211; called background &amp;#8211; and observations using an optimum interpolation (OI) scheme. Such method requires the specification of observations and background errors. In general, constant standard deviation errors are used but more errors are made when high precipitation are observed. Then, to take this effect into account and to avoid a model over-estimation in case of light precipitation, a variable formula of the observation standard deviation error was purposed with a small value for null precipitation and greater values when precipitation are higher, following a linear equation.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; Desroziers et al proposed a method to determine observations and background errors called a posteriori diagnosis. To use this iterative method, the analysis has to be ran several times until it converged. In this study, the a posteriori diagnosis is used per precipitation class to determine the observation standard deviation error formula. MESCAN was tested using the French operational model AROME at 1,3km resolution and the atmopsheric UERRA analysis downscaled to 5,5km background and combined to the French observational network over the 2016-2018 period. The observation standard deviation error formula obtained by the a posteriori diagnosis is then used in the MESCAN analysis system to produce precipitation analysis over the 2016-2018 period. Results are compared to UERRA precipitation reanalysis over independant observations by comparing bias, RMSE and scores per precipitation class.&lt;/em&gt;&lt;/p&gt;

Raindrop Size Spectrum in Deep Convective Regions of the Americas

This study compared drop size distribution (DSD) measurements on the surfaces, the corresponding properties, and the precipitation modes among three deep convective regions within the Americas. The measurement compilation corresponded to two sites in the midlatitudes: the U.S. Southern Great Plains and Córdoba Province in subtropical South America, as well as to one site in the tropics: Manacapuru in central Amazonia; these are all areas where intense rain-producing systems contribute to the majority of rainfall in the Americas’ largest river basins. This compilation included two types of disdrometers (Parsivel and 2D-Video Disdrometer) that were used at the midlatitude sites and one type of disdrometer (Parsivel) that was deployed at the tropical site. The distributions of physical parameters (such as rain rate R, mass-weighted mean diameter Dm, and normalized droplet concentration Nw) for the raindrop spectra without rainfall mode classification seemed similar, except for the much broader Nw distributions in Córdoba. The raindrop spectra were then classified into a light precipitation mode and a precipitation mode by using a cutoff at 0.5 mm h−1 based on previous studies that characterized the full drop size spectra. These segregated rain modes are potentially unique relative to previously studied terrain-influenced sites. In the light precipitation and precipitation modes, the dominant higher frequency observed in a broad distribution of Nw in both types of disdrometers and the identification of shallow light precipitation in vertically pointing cloud radar data represent unique characteristics of the Córdoba site relative to the others. As a result, the co-variability between the physical parameters of the DSD indicates that the precipitation observed in Córdoba may confound existing methods of determining the rain type by using the drop size distribution.

Assessing the Impact of Light/Shallow Precipitation Retrievals from Satellite-Based Observations Using Surface Radar and Micro Rain Radar Observations

The accurate representation of precipitation across the Earth’s surface is crucial to furthering our knowledge and understanding of the Earth System and its component processes. Precipitation poses a number of challenges, particularly due to the variability of precipitation over time and space and whether it falls as snow or rain. While conventional measures of precipitation are reasonably good at the location of their measurement, their distribution across the Earth’s surface is uneven with some regions having no surface measurements. Spaceborne sensors have the capability of providing regular observations across the Earth’s surface that can provide estimates of precipitation. However, the estimation of precipitation from satellite observations is not necessarily straightforward. Visible and/or infrared techniques rely upon imprecise cloud-top to surface precipitation relationships, while the sensitivity of passive microwave techniques to different precipitation types is not consistent. Active microwave (radar) observations provide the most direct satellite measurements of precipitation but cannot provide estimates close to the surface and are generally not sufficiently sensitive to resolve light precipitation. This is particularly problematic at mid to high latitudes, where light and/or shallow precipitation dominates. This paper compares measurements made by ground-based weather radars, Micro Rain Radars and the spaceborne Dual-frequency Precipitation Radar to study both light precipitation intensity and shallow precipitation occurrence and to assess their impact on satellites retrievals of precipitation at the mid to high latitudes.

Comparison of Monthly IMERG Precipitation Estimates with PACRAIN Atoll Observations

Satellite-based precipitation estimates provide valuable information where surface observations are not readily available, especially over the large expanses of the ocean where in-situ precipitation observations are very sparse. This study compares monthly precipitation estimates from the Integrated Multi-satellitE Retrievals for GPM (IMERG) with gauge observations from 37 low-lying atolls from the Pacific Rainfall Database for the period June 2000 – August 2020. Over the analysis period, IMERG estimates are slightly higher than the atoll observations by 0.67% with a monthly correlation of 0.68. Seasonally, DJF shows excellent agreement with a near-zero bias, while MAM shows IMERG is low by 4.6%, and JJA is high by 1.2%. SON exhibits the worst performance, with IMERG overestimating by 6.5% compared to the atolls. The seasonal correlations are well-contained in the range 0.67 – 0.72, with the exception of SON at 0.62. Furthermore, SON has the highest RMSE at 4.70 mm/day, making it the worst season for all metrics. Scatterplots of IMERG versus atolls show IMERG, on average, is generally low for light precipitation accumulations and high for intense precipitation accumulations, with best agreement at intermediate rates. Seasonal variations exist at light and intermediate rate accumulations, but IMERG consistently overestimates at intense precipitation rates. The differences between IMERG and atolls varies over time but does not exhibit any discernable trend or dependence on atoll population. The PACRAIN atoll gauges are not wind-loss corrected, so application of an appropriate adjustment would increase the precipitation amounts compared to IMERG. These results provide useful insight to users as well as valuable information for future improvements to IMERG.

The effect of different contributing sensors in IMERG-Final precipitation estimates

&lt;p&gt;Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) final-product V06B with a ground-radar product, Multi-Radar Multi-Sensor (MRMS), over eastern United-States during the hurricane days that occurred in 2016-2018 using both pixel-based and object-based approaches. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS in comparison with the temporally averaged MRMS data (MRMS-Averaged) with a bias reduction of 75% and 65%, respectively. PMW observations tend to show storms with smaller areas in the IMERG final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1mm/hr) in the object-based approach, hurricane objects in the IMERG final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates in the IMERG final product have smaller areas with higher average intensity during the PMW observations compared to data estimated by Morph or IR (morph/IR) observations. It is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. The quality of IMERG data changes with the longer absence of the PMW observations. IMERG data estimated by morph/IR observations, with a 30-minute time-distance to the nearest PMW observation, showed the best agreement with MRMS-Averaged even in comparison with PMW estimates, possibly due to the time-lag in recording the precipitation between satellites and ground-radars. It is also possible to be related to the homogenizing nature of morphing technique in IMERG and averaging MRMS data in time in MRMS-Averaged, relaxing the differences between PMW observations and MRMS. However, the morph/IR data quality deteriorates with the longer absence of PMW sensors. The inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area.&lt;/p&gt;

Urbanization Significantly Reduces Frequency of Light Rain: an Example From Beijing City

An hour precipitation dataset of 42 automatic weather stations is developed and applied to analyze the temporal and spatial characteristics of light precipitation in urban areas of Beijing City during 2007-2017. The stations are classified into three groups, including 18 sites in central urban area (4th RR), 10 sites in peri-urban area (4th-5th RR) and 14 sites in suburban area (5th-6th RR). Light precipitation is defined as hourly rainfall of 0.1-0.3mm. Analysis shows that light precipitation occurred in urban area in the whole day, with the peak value in 0600 LST and minimum value in 1600LST; monthly variation of light precipitation frequency (LPF) was characterized by the highest value in summer and the lowest value in winter; remarkable differences are found for the various urbanized areas, with the annual and seasonal mean LPF being generally small in central urban area and gradually increasing toward suburban area; the hourly mean LPF during morning and nighttime is higher in summer than those in other seasons in each of the urban areas; relative humidity, aerosol and wind speed might have been the major influential factors for the observed temporal and spatial pattern of light precipitation.

Observational Analysis of Aerosol–Meteorology Interactions for the Severe Haze Episode in Korea

Korea has occasionally suffered from various kinds of severe hazes such as long-range transported aerosol (LH), yellow sand (YS), and urban haze (UH). We classified haze days into LH, YS, and UH and analyzed the characteristics of its associated meteorological conditions for 2011–2016 using reanalysis data and surface observations. The results show that higher boundary layer height and stronger wind speed were found for the LH and YS hazes relative to those for UH. Intensive analysis on a golden episode of 10–18 January 2013 indicates that the cloud fraction increased along with extended light precipitation at a weaker rate by enhanced aerosol loading for an unprecedented LH event, which in turn brought about a decrease in boundary layer height (BLH) with less irradiance, that is, much stronger stability. Later, the intensified stability after the LH event accumulated and increased domestic aerosols, and eventually resulted in the longer-lasting severe haze. This study suggests that aerosol–meteorology interactions play an important role in both short-term weather and fine particle forecasts, especially on polluted days.