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has since remained around 680. To avoid the potential effects of this rapid change in observatory numbers, only data from the period 1958–2012 were analyzed here. The precipitation data in Fig. 1a on 5 July 2013 are derived from the China NMIC merged precipitation dataset of Chinese auto–weather station precipitation and the U.S. Climate Prediction Center morphing technique (CMORPH) precipitation product ( Pan et al. 2012 ), which includes precipitation over the East Asian continent and ocean
has since remained around 680. To avoid the potential effects of this rapid change in observatory numbers, only data from the period 1958–2012 were analyzed here. The precipitation data in Fig. 1a on 5 July 2013 are derived from the China NMIC merged precipitation dataset of Chinese auto–weather station precipitation and the U.S. Climate Prediction Center morphing technique (CMORPH) precipitation product ( Pan et al. 2012 ), which includes precipitation over the East Asian continent and ocean
outcome of a seminar conducted by the “Tiger Team,” and the report was distributed ( WMO 2013 ). After that, in 2014, CMA drafted the FY-3E Mission Requirement for the FY-3 Third Phase Program, which involves four satellites, one of which is an EM satellite. The program was approved and funded in 2018 ( Zhang et al. 2022 ). Besides meteorology and oceanography, space weather is also considered in the FY-3E mission. There are 11 instruments in total (see Table 1 ) on board FY-3E and almost
outcome of a seminar conducted by the “Tiger Team,” and the report was distributed ( WMO 2013 ). After that, in 2014, CMA drafted the FY-3E Mission Requirement for the FY-3 Third Phase Program, which involves four satellites, one of which is an EM satellite. The program was approved and funded in 2018 ( Zhang et al. 2022 ). Besides meteorology and oceanography, space weather is also considered in the FY-3E mission. There are 11 instruments in total (see Table 1 ) on board FY-3E and almost
seeds and earth to food. Wildland fires could change all of that. Of course our understanding of these issues has greatly evolved, and this chapter treats how that understanding has progressed over the past 100 years. We now understand that the sun not only provides Earth’s energy, but also produces space weather that impacts Earth and its atmosphere. The rapid increase of available environmental data has enabled rapid advances in our understanding of processes. Similarly, advances in computational
seeds and earth to food. Wildland fires could change all of that. Of course our understanding of these issues has greatly evolved, and this chapter treats how that understanding has progressed over the past 100 years. We now understand that the sun not only provides Earth’s energy, but also produces space weather that impacts Earth and its atmosphere. The rapid increase of available environmental data has enabled rapid advances in our understanding of processes. Similarly, advances in computational
, there are at present no operational observations of thermospheric constituents to assimilate as initial conditions and verification for these constituent forecasts, suggesting both questionable and largely unverifiable thermospheric skill impacts. With these realities in mind, while also recognizing new needs to extend NWP models into the thermosphere to support space weather applications (e.g., Jackson et al. 2019 ; Berger et al. 2020 ), here we seek simpler and less intrusive dynamical core
, there are at present no operational observations of thermospheric constituents to assimilate as initial conditions and verification for these constituent forecasts, suggesting both questionable and largely unverifiable thermospheric skill impacts. With these realities in mind, while also recognizing new needs to extend NWP models into the thermosphere to support space weather applications (e.g., Jackson et al. 2019 ; Berger et al. 2020 ), here we seek simpler and less intrusive dynamical core
/climate model (e.g., Arribas et al. 2011 ; MacLachlan et al. 2015 ; Swinbank et al. 2016 ), or alternatively by fitting an appropriate statistical model to past data [e.g., for solar flare forecasts ( Bloomfield et al. 2012 ); for forecasts of relativistic electrons at geostationary orbits ( Baker et al. 1990 ; Boynton et al. 2016 )]. Motivated by these needs for space weather, this study presents a novel statistical approach for issuing probabilistic forecasts of categorical events and will demonstrate
/climate model (e.g., Arribas et al. 2011 ; MacLachlan et al. 2015 ; Swinbank et al. 2016 ), or alternatively by fitting an appropriate statistical model to past data [e.g., for solar flare forecasts ( Bloomfield et al. 2012 ); for forecasts of relativistic electrons at geostationary orbits ( Baker et al. 1990 ; Boynton et al. 2016 )]. Motivated by these needs for space weather, this study presents a novel statistical approach for issuing probabilistic forecasts of categorical events and will demonstrate
Ground Validation of Surface Snowfall Algorithm in GPM Dual-Frequency Precipitation Radarrate. Brief algorithm descriptions are available in section 2 . The algorithm provides a surface snowfall flag (1 or 0 product) at each valid DPR Ku- and Ka-band matched footprint. Le et al. (2017) showed initial qualitative evaluations of the algorithm with promising results when compared to some of the Next Generation Weather Radars (NEXRAD; or WSR-88D). In this paper, we focus on performing more extensive ground validations in both qualitative and quantitative manner with NEXRAD, NASA
rate. Brief algorithm descriptions are available in section 2 . The algorithm provides a surface snowfall flag (1 or 0 product) at each valid DPR Ku- and Ka-band matched footprint. Le et al. (2017) showed initial qualitative evaluations of the algorithm with promising results when compared to some of the Next Generation Weather Radars (NEXRAD; or WSR-88D). In this paper, we focus on performing more extensive ground validations in both qualitative and quantitative manner with NEXRAD, NASA
eventual establishment of civilizations. Fig. 1. The solar atmosphere viewed in EUV light shows a wide range of magnetic structures. This composite false-color image, combining data captured by the Solar Dynamics Observatory in different filters of the Atmospheric Imaging Assembly, shows active regions (white), coronal holes (blue), and a million-km-long cold filament suspended in 1-MK plasma (red). The image was taken at 2214 UTC 2 Feb 2012. Space weather scientists research the effects of solar
eventual establishment of civilizations. Fig. 1. The solar atmosphere viewed in EUV light shows a wide range of magnetic structures. This composite false-color image, combining data captured by the Solar Dynamics Observatory in different filters of the Atmospheric Imaging Assembly, shows active regions (white), coronal holes (blue), and a million-km-long cold filament suspended in 1-MK plasma (red). The image was taken at 2214 UTC 2 Feb 2012. Space weather scientists research the effects of solar
The solar wind is a continuous, varying outflow of high-temperature plasma, traveling at hundreds of kilometers per second and stretching to over 100 au from the Sun. This paper is the third in a series of five papers about understanding the phenomena that cause space weather and their impacts. These papers are a primer for meteorological and climatological students who may be interested in the outside forces that directly or indirectly affect Earth’s neutral atmosphere. The series is also
The solar wind is a continuous, varying outflow of high-temperature plasma, traveling at hundreds of kilometers per second and stretching to over 100 au from the Sun. This paper is the third in a series of five papers about understanding the phenomena that cause space weather and their impacts. These papers are a primer for meteorological and climatological students who may be interested in the outside forces that directly or indirectly affect Earth’s neutral atmosphere. The series is also
and thermal noise. 2. Methodology a. Considerations for SA weather radar Spaced antenna concepts have been reviewed in several works ( Briggs et al. 1950 ; Briggs 1985 ; Larsen and Röttger 1989 ; Doviak et al. 1996 ; Holloway et al. 1997 ; Zhang and Doviak 2007 ), and can be explained through the cross-correlation function of backscattered electric fields sampled by two monostatic antenna systems A 1 and A 2 separated by a baseline Δ x . For SA wind estimation, the idea is that the
and thermal noise. 2. Methodology a. Considerations for SA weather radar Spaced antenna concepts have been reviewed in several works ( Briggs et al. 1950 ; Briggs 1985 ; Larsen and Röttger 1989 ; Doviak et al. 1996 ; Holloway et al. 1997 ; Zhang and Doviak 2007 ), and can be explained through the cross-correlation function of backscattered electric fields sampled by two monostatic antenna systems A 1 and A 2 separated by a baseline Δ x . For SA wind estimation, the idea is that the
Electronic Scan Strategy for Phased Array Weather Radar Using a Space–Time Characterization Modelalso contains an optimized scheduling algorithm for the PAWR, and an example of its implementation with a comparison of results to mechanically steered beam weather radar is introduced. Section 6 summarizes the main results of this work. 2. Space–time characterization model for precipitation a. Spatial scales in precipitation systems Figure 1 presents a relative scale map for different high-impact weather phenomena, exhibiting the connection between space scales and time scales. The figure shows
also contains an optimized scheduling algorithm for the PAWR, and an example of its implementation with a comparison of results to mechanically steered beam weather radar is introduced. Section 6 summarizes the main results of this work. 2. Space–time characterization model for precipitation a. Spatial scales in precipitation systems Figure 1 presents a relative scale map for different high-impact weather phenomena, exhibiting the connection between space scales and time scales. The figure shows
