DeepPrecip: A deep neural network for precipitation retrievals

Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval from a deep convolutional neural network (DeepPrecip) to estimate 20-minute average surface precipitation accumulation using near-surface radar data inputs. DeepPrecip displays high retrieval skill and can accurately model total precipitation accumulation, with a mean square error (MSE) 99 % lower, on average, than current methods. DeepPrecip also outperforms a less complex machine learning retrieval algorithm, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses suggest that a combination of both near-surface (below 1 km) and higher-altitude (1.5 &ndash; 2 km) radar measurements are the primary features contributing to retrieval accuracy. Further, DeepPrecip closely captures total precipitation accumulation magnitudes and variability across nine distinct locations without requiring any explicit descriptions of particle microphysics or geospatial covariates. This research reveals the important role for deep learning in extracting relevant information about precipitation from atmospheric radar retrievals.</p

Improving active remote sensing retrievals of snowfall at microwave wavelengths: an emphasis on the global precipitation measurement mission’s dual-frequency precipitation radar

Even though snowfall at the surface is often constrained to higher latitudes or altitudes, the contribution of solid-phase hydrometeors to the hydrologic cycle is not trivial and can be related to more than 50% of all surface rain events. Furthermore, the quantification of snow and ice in the atmospheric column is required to understand the Earth’s outgoing thermal radiative budget. Thus, the retrieval of snowfall from spaceborne radars that can sample remote regions of the world is invaluable for both atmospheric and climate sciences. One spaceborne radar capable of measuring snowfall is the Global Precipitation Measurement mission’s Dual-frequency Precipitation Radar (GPM-DPR). Initial evaluations of the retrieval of near-surface snowfall from GPM-DPR against the common global snowfall reference (i.e., CloudSat) showed large discrepancies between the two radar retrieval estimates. The large discrepancy between the CloudSat and GPM-DPR snowfall retrieval served as the main motivation for the work conducted here. Three tasks were formulated and conducted in this dissertation: (1) Evaluate the assumptions within the current GPM-DPR retrieval of snowfall; (2) Create an alternative retrieval for GPM-DPR; (3) Compare the new retrieval to the old retrieval methods. Task 1 is found in Chapter 2, Task 2 is in Chapter 3 and Task 3 is in Chapter 4. For Task 1, the investigation of ground-based measurements of both rain and snow and their particle size distributions allowed for the assessment of the main microphysical assumption of the GPM-DPR retrieval, which assumes that all hydrometeors obey the same empirical relationship between the precipitation rate (R) and the mass-weighted mean diameter (D_m). Rainfall observations showed that the default R-D_m relation for rainfall is plausible and shows general consistency with a Pearson ρ correlation coefficient of 0.63. However, snowfall observations showed that the R-D_m relation does not apply well for snowfall resulting in the underestimation of R. Furthermore, the low correlation between the log⁡〖(R〗) and D_m (ρ=0.23) suggests that an R-D_m retrieval is not optimal for snowfall retrievals and other methods should be explored. Motivated from the results of Task 1, an alternative retrieval for GPM-DPR was designed in Task 2 using a neural network, state-of-the-art particle scattering models and measured particle size distributions. The main result from Task 2 is that the neural network retrieval significantly improves (p<0.05) the mean squared error of the retrieval of ice water content (IWC) compared to old power-law methods and an estimate of the current GPM-DPR algorithm. This was shown in the evaluation of the retrieval on a subset of synthetic data that was not used in training the neural network as well as in three case studies from NASA field campaigns where independent observations of radar reflectivity and in-situ parameters were made. Finally, Task 3 evaluated the newly formulated retrieval from Task 2 against the operational CloudSat product (2C-SNOWPROFILE) and the current GPM-DPR algorithm. The evaluation is done using a premade coincident dataset of both CloudSat and GPM-DPR which allowed for the direct comparison of all retrieval methods. Comparing the three retrievals show that on average the neural network retrieval performs best, predicting R just below the melting layer to within 2%. A secondary result from Task 3 is that the 2C-SNOWPROFLE retrieval is likely underestimating R for moderate to intense snowfall events signified by a 35% reduction of R from -15°C to the melting layer

Developing a radar-based machine learning snowfall retrieval algorithm

As global temperatures continue to rise, snowfall patterns are expected to respond in a complex, nonlinear manner. Changes in the quantity of snowmelt-derived freshwater will impact global water-energy budgets, flood frequencies and intensities, and regional water resource management practices. Remote sensing is an observational alternative to poorly constrained reanalysis-derived estimates of snow water equivalent (SWE) across Arctic regions. We present an outlier detection methodology which leverages remote sensing data from CloudSat to constrain reanalysis product estimates of SWE. This analysis highlights areas and periods of high uncertainty in the gridded reanalysis datasets, and identifies a systematic positive SWE bias (of 14.9%) in a blended reanalysis product as a consequence of these low-quality estimates. The ability to use remotely sensed observations to characterize error in surface SWE estimates is incredibly powerful, however, remote sensing datasets are not without their own sources of uncertainty. This work also, therefore, examines the capabilities of a machine learning snowfall retrieval algorithm trained on vertically pointing surface radar data at a Global Precipitation Measurement (GPM) validation experiment site in southern Ontario. Random forest (RF) retrieval performance is compared to an ensemble of traditional Ze-S power law relationships, with the RF consistently displaying the lowest overall error. The RF also demonstrates a level of robustness not present in the power law relations, with low error when applied to unseen observations from a study site with a different regional climate. We further extend this methodology to a general machine learning precipitation retrieval across the wider northern hemisphere (NH) using additional input covariates, data from multiple sites over a longer time period, and by adopting a more sophisticated deep learning paradigm. The DeepPrecip retrieval algorithm displays a 187% improvement in snowfall retrieval accuracy when compared to traditional Ze-S/R power law relationships, and a 21% improvement over the aforementioned RF. The highly generalized nature of DeepPrecip facilitates its application to unseen data with only a small performance reduction. DeepPrecip also provides insight into the regions of the vertical column (below 1 km and between 1.5−2 km) most effective in contributing to high retrieval accuracy, highlighting the important role of ML in current and future spaceborne remote sensing precipitation missions

Precipitation characteristics and associated weather conditions on the eastern slopes of the Canadian Rockies during March–April 2015

Precipitation events that bring rain and snow to the Banff–Calgary area of Alberta are a critical aspect of the region's water cycle and can lead to major flooding events such as the June 2013 event that was the second most costly natural disaster in Canadian history. Because no special atmospheric-oriented observations of these events have been made, a field experiment was conducted in March and April 2015 in Kananaskis, Alberta, to begin to fill this gap. The goal was to characterize and better understand the formation of the precipitation at the surface during spring 2015 at a specific location in the Kananaskis Valley. Within the experiment, detailed measurements of precipitation and weather conditions were obtained, a vertically pointing Doppler radar was deployed and weather balloons were released. Although 17 precipitation events occurred, this period was associated with much less precipitation than normal (−35&thinsp;%) and above-normal temperatures (2.5&thinsp;°C). Of the 133&thinsp;h of observed precipitation, solid precipitation occurred 71&thinsp;% of the time, mixed precipitation occurred 9&thinsp;% and rain occurred 20&thinsp;%. An analysis of 17&thinsp;504 precipitation particles from 1181 images showed that a wide variety of crystals and aggregates occurred and approximately 63&thinsp;% showed signs of riming. This was largely independent of whether flows aloft were upslope (easterly) or downslope (westerly). In the often sub-saturated surface conditions, hydrometeors containing ice occurred at temperatures as high as 9&thinsp;°C. Radar structures aloft were highly variable with reflectivity sometimes  &gt; 30&thinsp;dBZe and Doppler velocity up to −1&thinsp;m&thinsp;s−1, which indicates upward motion of particles within ascending air masses. Precipitation was formed in this region within cloud fields sometimes having variable structures and within which supercooled water at least sometimes existed to produce accreted particles massive enough to reach the surface through the relatively dry sub-cloud region.</p

Atmospheric Research 2016 Technical Highlights

Atmospheric research in the Earth Sciences Division (610) consists of research and technology development programs dedicated to advancing knowledge and understanding of the atmosphere and its interaction with the climate of Earth. The Divisions goals are to improve understanding of the dynamics and physical properties of precipitation, clouds, and aerosols; atmospheric chemistry, including the role of natural and anthropogenic trace species on the ozone balance in the stratosphere and the troposphere; and radiative properties of Earth's atmosphere and the influence of solar variability on the Earth's climate. Major research activities are carried out in the Mesoscale Atmospheric Processes Laboratory, the Climate and Radiation Laboratory, the Atmospheric Chemistry and Dynamics Laboratory, and the Wallops Field Support Office. The overall scope of the research covers an end-to-end process, starting with the identification of scientific problems, leading to observation requirements for remote-sensing platforms, technology and retrieval algorithm development; followed by flight projects and satellite missions; and eventually, resulting in data processing, analyses of measurements, and dissemination from flight projects and missions. Instrument scientists conceive, design, develop, and implement ultraviolet, infrared, optical, radar, laser, and lidar technology to remotely sense the atmosphere. Members of the various laboratories conduct field measurements for satellite sensor calibration and data validation, and carry out numerous modeling activities. These modeling activities include climate model simulations, modeling the chemistry and transport of trace species on regional-to-global scales, cloud resolving models, and developing the next-generation Earth system models. Satellite missions, field campaigns, peer-reviewed publications, and successful proposals are essential at every stage of the research process to meeting our goals and maintaining leadership of the Earth Sciences Division in atmospheric science research. Figure 1.1 shows the 22-year record of peer-reviewed publications and proposals among the various laboratories

Vertical structure and microphysical observations of winter precipitation in an inner valley during the Cerdanya-2017 field campaign

Precipitation processes at windward and leeward sides of the mountains have been object of study for many decades. Instead, inner mountain valleys, where usually most mountain population lives, have received considerably less attention. This article examines precipitation processes during a winter field campaign in an inner valley of the Pyrenees (NE Spain) using, among other instruments, a K-band vertically pointing Doppler radar (Micro Rain Radar) and a laser-based optical disdrometer (Parsivel). A decoupling is found between the stalled air of the valley and the air of the free atmosphere above the mountain crest level, evidenced by an increase of turbulence and spectral width of precipitation particles. Wind shear layer may promote riming and aggregation of the ice and snow particles. Two main rainfall regimes are found during the campaign: (1) stratiform rainfall mostly produced by water vapour deposition processes, although sometimes riming and aggregation become important, and (2) weak convection with slight dominance of collision-coalescence processes. Precipitation characteristics at the bottom of the valley show typical continental features such as low Liquid Water Content, despite the valley is only about 100 km from the sea. This study demonstrates that inner valley may present distinct precipitation features with respect to windward and leeward precipitation

Atmospheric Instrument Systems and Technology in the Goddard Earth Sciences Division

Studies of the Earths atmosphere require a comprehensive set of observations that rely on instruments flown on spacecraft, aircraft, and balloons as well as those deployed on the surface. Within NASAs Goddard Space Flight Center (GSFC) Earth Sciences Division-Atmospheres, laboratories and offices maintain an active program of instrument system development and observational studies that provide: 1) information leading to a basic understanding of atmospheric processes and their relationships with the Earths climate system, 2) prototypes for future flight instruments, 3) instruments to serve as calibration references for satellite missions, and 4) instruments for future field validation campaigns that support ongoing space missions. Our scientists participate in all aspects of instrument activity, including component and system design, calibration techniques, retrieval algorithm development, and data processing systems. The Atmospheres Program has well-equipped labs and test equipment to support the development and testing of instrument systems, such as a radiometric calibration and development facility to support the calibration of ultraviolet and visible (UV/VIS), space-borne solar backscatter instruments. This document summarizes the features and characteristics of 46 instrument systems that currently exist or are under development. The report is organized according to active, passive, or in situ remote sensing across the electromagnetic spectrum. Most of the systems are considered operational in that they have demonstrated performance in the field and are capable of being deployed on relatively short notice. Other systems are under study or of low technical readiness level (TRL). The systems described herein are designed mainly for surface or airborne platforms. However, two Cubesat systems also have been developed through collaborative efforts. The Solar Disk Sextant (SDS) is the single balloon-borne instrument. The lidar systems described herein are designed to retrieve clouds, aerosols, methane, water vapor pressure, temperature, and winds. Most of the lasers operate at some wavelength combination of 355, 532, and 1064 nm. The various systems provide high sensitivity measurements based on returns from backscatter or Raman scattering including intensity and polarization. Measurements of the frequency (Doppler) shift of light scattered from various atmospheric constitutes can also be made. Microwave sensors consist of both active (radar) and passive (radiometer) systems. These systems are important for studying processes involving water in various forms. The dielectric properties of water affect microwave brightness temperatures, which are used to retrieve atmospheric parameters such as rainfall rate and other key elements of the hydrological cycle. Atmosphere radar systems operate in the range from 9.6 GHz to 94 GHz and have measurement accuracies from -5 to 1 dBZ; radiometers operate in the 50 GHz to 874 GHz range with accuracies from 0.5 to 1 degree K; conical and cross-track scan modes are used. Our passive optical sensors, consisting of radiometers and spectrometers, collectively operate from the UV into the infrared. These systems measure energy fluxes and atmospheric parameters such as trace gases, aerosols, cloud properties, or altitude profiles of various species. Imager spatial resolution varies from 37 m to 400 m depending on altitude; spectral resolution is as small as 0.5 nm. Many of the airborne systems have been developed to fly on multiple aircraft