Satellite-Based Precipitation Estimates Validation Using Surface Stations in the Central Region of the State of São Paulo, From 1981 to 2019

With the advance of remote sensing technologies, meteorological satellites have become an alternative in the process of monitoring and measuring meteorological variables, both spatially and temporally. The present study brings some additional elements to the validation of satellite-based precipitation estimates by evaluating the CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station) monthly product for the central region of the state of São Paulo, Brazil, in the period 1981-2019. Initially, the general relationship between satellite estimates and surface rainfall data is assessed using the linear adjustment and error analysis in both temporal and spatial perspectives, followed by a trend analysis using Laplace test. The monthly map analysis showed a better performance of CHIRPS during the dry period (April to August) than for the wet period (October to March). Finally, monthly trends showed, in general, the same pattern of variability in rainfall over 38 years and a prevalence toward the reduction of rainfall. In summary, CHIRPS product seems a reasonable alternative for regions that lack historical rainfall information.

Identification of key parameters producing rainfall

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Error Analysis and Modeling of GPM Dual-Frequency Precipitation Radar Near-surface Rainfall Product

The error characterization of rainfall products of spaceborne radar is essential for better applications of radar data, such as multi-source precipitation data fusion and hydrological modeling. In this study, we analyzed the error of the near-surface rainfall product of the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement Mission (GPM) and modeled it based on ground C-band dual-polarization radar (CDP) data with optimization rainfall retrieval. The comparison results show that the near-surface rainfall data were overestimated by light rain and slightly underestimated by heavy rain. The error of near-surface rainfall of the DPR was modeled as an additive model according to the comparison results. The systematic error of near-surface rainfall was in the form of a quadratic polynomial, while the systematic error of stratiform precipitation was smaller than that of convective precipitation. The random error was modeled as a Gaussian distribution centered at −1−0 mm h−1. The standard deviation of the Gaussian distribution of convective precipitation was 1.71 mm h−1 and the standard deviation of stratiform precipitation was 1.18 mm h−1, which is smaller than that of convective precipitation. In view of the precipitation retrieval algorithm of DPR, the error causes were analyzed from the reflectivity factor (Z) and the drop size distribution (DSD) parameters (Dm, Nw). The high accuracy of the reflectivity factor measurement results in a small systematic error. Importantly, the negative bias of Nw was very obvious when the rain type was convective precipitation, resulting in a large random error.

Microphysical process of precipitating hydrometeors from warm-front mid-level stratiform clouds revealed by ground-based lidar observations

Mid-level stratiform precipitations during the passage of warm front were detailedly observed on two occasions (light and moderate rain) by a 355-nm polarization lidar and water-vapor Raman lidar, both equipped with waterproof transparent roof windows. The hours-long precipitation streaks shown in the lidar signal (X) and volume depolarization ratio (δv) reveal some ubiquitous features of the microphysical process of precipitating hydrometeors. We find that for the light rain case, surface rainfall begins as supercooled liquid-drop-dominated hydrometeors fall out of their liquid parent cloud at altitudes above the 0 °C level, and most liquid drops quickly freeze into ice particles (δv > 0.25) during the first 100–200 m of their descent, where humid aerosol particles exist. Subsequently, the falling hydrometeors yield a dense layer with an ice/snow bright band occurring above and a liquid-water bright band occurring below (separated by a lidar dark band) as a result of crossing the 0 °C level. The ice/snow bright band might be a manifestation of local hydrometeor accumulation. Most falling raindrops shrink or vanish in the liquid-water bright band due to evaporation, whereas a few large raindrops fall out of the layer. We also find that a prominent depolarization δv peak (0.10–0.35) always occurs at an altitude of approximately 0.6 km during surface rainfall, reflecting the collision-coalescence growth of falling large raindrops and their subsequent spontaneous breakup. The microphysical process (at ice-bright-band altitudes and below) of moderate rain resembles that of the light rain case, but more large-sized hydrometeors are involved.

The Fresnel Platform for Greater Paris: online tool to Dynamically Manage Multiscale Urban Resilience

&lt;p&gt;The general goal of the Fresnel platform of Ecole des Ponts ParisTech is to develop research and innovation on multiscale urban resilience. It is therefore conceived as a SaaS (Sofware as a Service) plaform providing data over a wide range of space-time scales and &amp;#160;appropriate softwares to analyse and simulate them over this range.&amp;#160;&lt;/p&gt;&lt;p&gt;The most recent development is the radar component RadX V3.0 that is now operational at https://radx.enpc.fr. It provides an easy access to various products based on precipitation measurement performed at the radial scale of 128 m by the ENPC polarimetric X-band radar. Using reliable and open source libraries it features a real-time radar display available to the general public and professionals who can freely access the precipitation data over a large part of &amp;#206;le-de-France region from their web browser (desktop and mobile). Another major component is the &quot;analysis&quot; section where scientists and managers &amp;#160;can define and select rainfall events in a interactive calendar and then analyse rainfall data throught different tools such as an interactive map with time control and dynamically genetared hyetograms.&lt;br&gt;For more refined spatial analysis registered users can also introduce their own shapefiles containing catchments and subcatchments, as well as to extract data and maps. They can also pinpoint a radar pixel to display hyetogram from a local area, this versatily on spatial and temporal selections allows for very precise analysis.&lt;/p&gt;&lt;p&gt;The application allows for different radar product analysis like DPSRI (Dual Polarization Surface Rainfall Intensity) and SRI (Surface Rainfall Intensity).These complementary products enhance the case studies analysis and give weather scientists more tools directly available from their web browser.&amp;#160;&lt;/p&gt;&lt;p&gt;Further software developments include high resolution hydrological modeling and a multifractal toolbox to estimate the scale invariant features of the precipitation and other fields (e.g. landuse). These developments are performed in close contacts and feedbacks from the scientific and professional world and they greatly benefit from the support of the Chair &amp;#8220;Hydrology for Resilient Cities&amp;#8221; (https://hmco.enpc.fr/portfolio-archive/chair-hydrology-for-resilient-cities/) endowed by the world leader industrial in water management and from previous EU framework programmes.&lt;/p&gt;

The effects of cloud–aerosol interaction complexity on simulations of presummer rainfall over southern China

Convection-permitting simulations are used to understand the effects of cloud–aerosol interactions in a case of heavy rainfall over southern China. The simulations are evaluated using radar observations from the Southern China Monsoon Rainfall Experiment (SCMREX) and remotely sensed estimates of precipitation, clouds and radiation. We focus on the effects of complexity in cloud–aerosol interactions, especially the depletion and transport of aerosol material by clouds. In particular, simulations with aerosol concentrations held constant are compared with a fully cloud–aerosol-interacting system to investigate the effects of two-way coupling between aerosols and clouds on a line of organised deep convection. It is shown that the cloud processing of aerosols can change the vertical structure of the storm by using up aerosols within the core of line, thereby maintaining a relatively clean environment which propagates with the heaviest rainfall. This induces changes in the statistics of surface rainfall, with a cleaner environment being associated with less-intense but more-frequent rainfall. These effects are shown to be related to a shortening of the timescale for converting cloud droplets to rain as the aerosol number concentration is decreased. The simulations are compared to satellite-derived estimates of surface rainfall, a condensed-water path and the outgoing flux of short-wave radiation. Simulations for fewer aerosol particles outperform the more polluted simulations for surface rainfall but give poorer representations of top-of-atmosphere (TOA) radiation.