Precipitation Protocols

The purpose of this activity is to determine the amount of moisture input to the local environment by measuring rain and snowfall and to measure the pH of precipitation. To do so students use a rain gauge and a snowboard to measure the daily amount of precipitation. Special pH measuring techniques for precipitation are used to determine the pH of rain and melted snow. Intended outcomes are that students will understand that precipitation is measured in depth and this depth is assumed to apply to a large area, that precipitation has a pH that can vary, and that snow is an input of water to the surface just like rain and each snowfall is equivalent to some amount of rainfall. Supporting background materials for both student and teacher are included. Educational levels: Primary elementary, Intermediate elementary, Middle school, High school

Spatiotemporal variation in precipitation during rainy season in Beibu Gulf, South China, from 1961 to 2016

The spatiotemporal variation in precipitation is an important part of water cycle change, which is directly associatedwith the atmospheric environment and climate change. The high-resolution spatiotemporal change of precipitation is still unknown inmany areas despite its importance. This study analyzed the spatiotemporal variation in precipitation in Beibu Gulf, South China, during the rainy season (fromApril to September) in the period of 1961–2016. The precipitation datawere collected from 12 national standard rain-gauge observation stations. The spatiotemporal variation in precipitation was evaluated with incidence rate and contribution rate of precipitation. The tendency of variations was analyzed using the Mann–Kendall method. The precipitation in the rainy season contributed 80% to the total annual precipitation. In general, there was an exponential decreasing tendency between the precipitation incidence rate and increased precipitation durations. The corresponding contribution rate showed a downward trend after an initial increase. The precipitation incidence rate decreased with the rising precipitation grades, with a gradual increase in contribution rate. The precipitation incidence rate and contribution rate of 7–9 d durations showed the significant downward trends that passed the 95% level of significance test. The results provide a new understanding of precipitation change in the last five decades, which is valuable for predicting future climate change and extreme weather prevention and mitigation

New 2012 Precipitation Frequency Estimation Analysis for Alaska: Musings on Data Used and the Final Product

INE/AUTC 13.1

The dependence of precipitation and its footprint on atmospheric temperature in idealized extratropical cyclones

Flood hazard is a function of the magnitude and spatial pattern of precipitation accumulation. The sensitivity of precipitation to atmospheric temperature is investigated for idealized extratropical cyclones, enabling us to examine the footprint of extreme precipitation (surface area where accumulated precipitation exceeds high thresholds) and the accumulation in different-sized catchment areas. The mean precipitation increases with temperature, with the mean increase at 5.40%/∘C. The 99.9th percentile of accumulated precipitation increases at 12.7%/∘C for 1 h and 9.38%/∘C for 24 h, both greater than Clausius-Clapeyron scaling. The footprint of extreme precipitation grows considerably with temperature, with the relative increase generally greater for longer durations. The sensitivity of the footprint of extreme precipitation is generally super Clausius-Clapeyron. The surface area of all precipitation shrinks with increasing temperature. Greater relative changes in the number of catchment areas exceeding extreme total precipitation are found when the domain is divided into larger rather than smaller catchment areas. This indicates that fluvial flooding may increase faster than pluvial flooding from extratropical cyclones in a warming world. When the catchment areas are ranked in order of total precipitation, the 99.9th percentile is found to increase slightly above Clausius-Clapeyron expectations for all of the catchment sizes, from 9 km2 to 22,500 km2. This is surprising for larger catchment areas given the change in mean precipitation. We propose that this is due to spatially concentrated changes in extreme precipitation in the occluded fron

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach

The sensitivity of oceanic precipitation to sea surface temperature

Our study forms the oceanic counterpart to numerous observational studies over land concerning the sensitivity of extreme precipitation to a change in air temperature. We explore the sensitivity of oceanic precipitation to changing sea surface temperature (SST) by exploiting two novel datasets at high resolution. First, we use the Ocean Rainfall And Ice-phase precipitation measurement Network (OceanRAIN) as an observational along-track shipboard dataset at 1 min resolution. Second, we exploit the most recent European Reanalysis version 5 (ERA5) at hourly resolution on a 31 km grid. Matched with each other, ERA5 vertical velocity allows the constraint of the OceanRAIN precipitation. Despite the inhomogeneous sampling along ship tracks, OceanRAIN agrees with ERA5 on the average latitudinal distribution of precipitation with fairly good seasonal sampling. However, the 99th percentile of OceanRAIN precipitation follows a super Clausius–Clapeyron scaling with a SST that exceeds 8.5 % K−1 while ERA5 precipitation scales with 4.5 % K−1. The sensitivity decreases towards lower precipitation percentiles, while OceanRAIN keeps an almost constant offset to ERA5 due to higher spatial resolution and temporal sampling. Unlike over land, we find no evidence for a decreasing precipitation event duration with increasing SST. ERA5 precipitation reaches a local minimum at about 26 ∘C that vanishes when constraining vertical velocity to strongly rising motion and excluding areas of weak correlation between precipitation and vertical velocity. This indicates that instead of moisture limitations as over land, circulation dynamics rather limit precipitation formation over the ocean. For the strongest rising motion, precipitation scaling converges to a constant value at all precipitation percentiles. Overall, high resolutions in observations and climate models are key to understanding and predicting the sensitivity of oceanic precipitation extremes to a change in SST

Precipitation regime change in Western North America: The role of Atmospheric Rivers.

Daily precipitation in California has been projected to become less frequent even as precipitation extremes intensify, leading to uncertainty in the overall response to climate warming. Precipitation extremes are historically associated with Atmospheric Rivers (ARs). Sixteen global climate models are evaluated for realism in modeled historical AR behavior and contribution of the resulting daily precipitation to annual total precipitation over Western North America. The five most realistic models display consistent changes in future AR behavior, constraining the spread of the full ensemble. They, moreover, project increasing year-to-year variability of total annual precipitation, particularly over California, where change in total annual precipitation is not projected with confidence. Focusing on three representative river basins along the West Coast, we show that, while the decrease in precipitation frequency is mostly due to non-AR events, the increase in heavy and extreme precipitation is almost entirely due to ARs. This research demonstrates that examining meteorological causes of precipitation regime change can lead to better and more nuanced understanding of climate projections. It highlights the critical role of future changes in ARs to Western water resources, especially over California

An object-based approach for verification of precipitation estimation

Verification has become an integral component in the development of precipitation algorithms used in satellite-based precipitation products and evaluation of numerical weather prediction models. A number of object-based verification methods have been developed to quantify the errors related to spatial patterns and placement of precipitation. In this study, an image processing technique known as watershed transformation, capable of detecting closely spaced, but separable precipitation areas, is adopted in the object-based approach. Several key attributes of the segmented precipitation objects are selected and interest values of those attributes are estimated based on the distance measurement of the estimated and reference images. An overall interest score is estimated from all the selected attributes and their interest values. The proposed object-based approach is implemented to validate satellite-based precipitation estimation against ground radar observations. The results indicate that the watershed segmentation technique is capable of separating the closely spaced local-scale precipitation areas. In addition, three verification metrics, including the object-based false alarm ratio, object-based missing ratio, and overall interest score, reveal the skill of precipitation estimates in depicting the spatial and geometric characteristics of the precipitation structure against observations

Precipitation detector Patent

Precipitation detector and mechanism for stopping and restarting machinery at initiation and cessation of rai