Search Results

You are looking at 1 - 10 of 16 items for :

  • Seasonal effects x
  • Global Precipitation Measurement (GPM): Science and Applications x
  • All content x
Clear All
Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

effect on the hydrological cycle and the transformation of precipitation from mainly solid to more liquid precipitation ( López-Moreno et al. 2016 ; Maturilli et al. 2013 ). Thus, to observe the effects of climate change better, there is an urgent need to monitor long-term snowfall at the northern high latitudes. However, especially in the Arctic, a ground-based observational precipitation network is scarce and the environmental conditions such as weather and orography are harsh for instrumentation

Open access
Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

collocates the ground reference 1-km-resolution Radarnet product to the 5-km-resolution DPR and CMB products during a 3-yr period, with the aim of assessing the quality of the products. Collocation of the products to a coarser 25-km resolution is also considered. Three years of GPM products offer the opportunity to consider seasonal and interannual variabilities. Furthermore, the fine horizontal resolution of the ground-observing system allows for an analysis of the effects of nonuniform beam filling

Open access
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

masked by the atmospheric and liquid water emissivity ( Kneifel et al. 2010 ; Johnson et al. 2016 ; Liu and Seo 2013 ; Wang et al. 2013 ; Panegrossi et al. 2017 ). On the other side, the snow-covered surface emissivity is extremely variable due to rapid changes of snow-cover extent, snow accumulation on the ground, and snowpack radiative properties, with significant effects on the snowfall microwave signal (e.g., Laviola et al. 2015 ; Prigent et al. 2003 ; Noh et al. 2009 ; Takbiri et al

Open access
Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

Resolution Imaging Spectroradiometer (MODIS) sensor to account for effects of the background snow-cover emission. We demonstrate that the algorithm shows improved skill in detection of snowfall over snow cover and can predict the likelihood of precipitation phase changes in the atmospheric boundary layer, which is not well observed by the GPM radar. In summary, the presented algorithm isolates a few physically relevant candidate vectors of brightness temperatures in the database via a weighted Euclidean

Full access
M. Petracca, L. P. D’Adderio, F. Porcù, G. Vulpiani, S. Sebastianelli, and S. Puca

global and consistent observations over the globe, including oceans and mountainous areas, and remote areas where ground-based precipitation measurements are scarce or not available, as in African areas. However, DPR measurements are affected by some limitations, such as attenuation, ground clutter, nonuniform beam filling (NUBF), and multiple scattering. These effects are taken into account in the NASA/JAXA algorithms used to retrieve DPR precipitation products ( Iguchi et al. 2017 ), but the

Full access
Catherine M. Naud, James F. Booth, Matthew Lebsock, and Mircea Grecu

intercomparison of cyclone-centered composites of surface precipitation obtained for both the NH and Southern Hemisphere (SH) 30°–60° latitude bands over the oceans. We discuss the impact of observational uncertainties and sampling-related issues as well as explore the sensitivity of precipitation in extratropical cyclones to environmental moisture amount and its implication for seasonal variations according to the three recent datasets: GPM-CMB, IMERG, and CloudSat . 2. Data sources In this study we explore

Full access
Robert A. Houze Jr., Lynn A. McMurdie, Walter A. Petersen, Mathew R. Schwaller, William Baccus, Jessica D. Lundquist, Clifford F. Mass, Bart Nijssen, Steven A. Rutledge, David R. Hudak, Simone Tanelli, Gerald G. Mace, Michael R. Poellot, Dennis P. Lettenmaier, Joseph P. Zagrodnik, Angela K. Rowe, Jennifer C. DeHart, Luke E. Madaus, Hannah C. Barnes, and V. Chandrasekar

measurements through a detailed examination of the vertical structure of precipitating clouds. The second goal is of great benefit to meteorology in general, as it extends beyond satellite applications to address fundamental aspects of orographic effects on precipitation processes. This article will focus on the second, more general goal, illustrating how the OLYMPEX dataset presents an opportunity to improve basic understanding of mountain effects on precipitation. However, these two goals are strongly

Open access
Gail Skofronick-Jackson, Mark Kulie, Lisa Milani, Stephen J. Munchak, Norman B. Wood, and Vincenzo Levizzani

backgrounds that have lower and more uniform emissivities ( Skofronick-Jackson et al. 2013 ), especially when compared to direct radar measurements. Indeed, a seasonal study of GMI falling snow retrievals has indicated a dependence on snow-cover characteristics ( Ebtehaj and Kummerow 2017 ). GMI, however, has high frequencies (166 and 183 GHz) that have been shown to be particularly useful for falling snow estimates ( Panegrossi et al. 2017 ). Datasets composed of near-coincident DPR–CPR observations have

Full access
Daniel J. Cecil and Themis Chronis

brightness temperature can be ambiguous because it could result from scattering by graupel or hail in a convective storm, or from a wet or water-covered surface. The interpretation is especially difficult when an overland scene includes convective storms, inland water bodies, and potentially even floodwater or wet soil from recent precipitation ( Fig. 1 ). This paper aims to enable more straightforward assessment of the impacts of hydrometeors on passive microwave measurements, by minimizing effects due

Full access
E. F. Stocker, F. Alquaied, S. Bilanow, Y. Ji, and L. Jones

month. The errors occurred when the solar beta angle (sun elevation above the orbit plane) was around 45°. Besides these occasional error spikes, the pitch and roll were susceptible to seasonal horizon radiance errors in the tenth-of-a-degree range. Yaw in the preboost period typically showed jumps of 0.1°–0.2° twice each orbit as the yaw was updated using the sun sensor data. The version 7 corrections also took out a sine wave–like component of the errors in roll and yaw because orbit period

Full access