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Airborne Doppler radar provides detailed and targeted observations of winds and precipitation in weather systems over remote or difficult-to-access regions that can help to improve scientific understanding and weather forecasts. Quality control (QC) is necessary to remove nonweather echoes from raw radar data for subsequent analysis. The complex decision-making ability of the machine learning random-forest technique is employed to create a generalized QC method for airborne radar data in convective weather systems. A manually QCed dataset was used to train the model containing data from the Electra Doppler Radar (ELDORA) in mature and developing tropical cyclones, a tornadic supercell, and a bow echo. Successful classification of ∼96% and ∼93% of weather and nonweather radar gates, respectively, in withheld testing data indicate the generalizability of the method. Dual-Doppler analysis from the genesis phase of Hurricane Ophelia (2005) using data not previously seen by the model produced a comparable wind field to that from manual QC. The framework demonstrates a proof of concept that can be applied to newer airborne Doppler radars.

Airborne Doppler radar is an invaluable tool for making detailed measurements of wind and precipitation in weather systems over remote or difficult to access regions, such as hurricanes over the ocean. Using the collected radar data depends strongly on quality control (QC) procedures to classify weather and nonweather radar echoes and to then remove the latter before subsequent analysis or assimilation into numerical weather prediction models. Prior QC techniques require interactive editing and subjective classification by trained researchers and can demand considerable time for even small amounts of data. We present a new machine learning algorithm that is trained on past QC efforts from radar experts, resulting in an accurate, fast technique with far less user input required that can greatly reduce the time required for QC. The new technique is based on the random forest, which is a machine learning model composed of decision trees, to classify weather and nonweather radar echoes. Continued efforts to build on this technique could benefit future weather forecasts by quickly and accurately quality-controlling data from other airborne radars for research or operational meteorology.

 

The interaction of airflow with complex terrain has the potential to significantly amplify extreme precipitation events and modify the structure and intensity of precipitating cloud systems. However, understanding and forecasting such events is challenging, in part due to the scarcity of direct in situ measurements. Doppler radar can provide the capability to monitor extreme rainfall events over land, but our understanding of airflow modulated by orographic interactions remains limited. The SAMURAI software is a three-dimensional variational data assimilation (3DVAR) technique that uses the finite element approach to retrieve kinematic and thermodynamic fields. The analysis has high fidelity to observations when retrieving flows over a flat surface, but the capability of imposing topography as a boundary constraint is not previously implemented. Here, we implement the immersed boundary method (IBM) as pseudo-observations at their native coordinates in SAMURAI to represent the topographic forcing and surface impermeability. In this technique, neither data interpolation onto a Cartesian grid nor explicit physical constraint integration during the cost function minimization is needed. Furthermore, the physical constraints are treated as pseudo-observations, offering the flexibility to adjust the strength of the boundary condition. A series of observing simulation sensitivity experiments (OSSEs) using a full-physics model and radar emulator simulating rainfall from Typhoon Chanthu (2021) over Taiwan are conducted to evaluate the retrieval accuracy and parameter settings. The OSSE results show that the strength of the IBM constraints can impact the overall wind retrievals. Analysis from real radar observations further demonstrates that the improved retrieval technique can advance scientific analyses for the underlying dynamics of orographic precipitation using radar observations.

 

Abstract The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future. Significance Statement During the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well. 

Tropical cyclone (TC) low‐level tangential wind structure is known to be an important input for risk assessment (e.g., storm surge). However, a realistic TC wind structure model with easy implementation is still needed for many applications. In this study, we obtain an inner‐core wind structure model purely from an empirical approach, which significantly reduces the complexity of TC wind profiles. From idealized simulations with different initial vortex structures, it is found that the absolute angular momentum (M) outside the radius of maximum wind (r_m) is quasi‐linear. This quasi‐linearMslope in a normalized coordinate decreases with TC intensity. The consequent linearMslope wind model can largely capture the simulated TC inner‐core wind structure and has the potential for many practical applications.

 

Abstract The parametric hurricane rainfall model (PHRaM), firstly introduced in 2007, has been widely used to forecast and quantify tropical-cyclone-induced rainfall (TC rainfall). The PHRaM is much more computationally efficient than global climate models, but PHRaM cannot be effectively utilized in the context of climate change because it does not have any parameters to capture the increase of tropospheric water vapor under the warming world. This study develops a new model that incorporates tropospheric water vapor to the PHRaM framework, named as the PHRaM with moisture (PHRaMM). The PHRaMM is trained to best fit the TC rainfall over the western North Pacific (WNP) unlike the PHRaM trained with the TCs over the continental US. The PHRaMM reliably simulates radial profile of TC rainfall and spatial distribution of accumulated rainfall during landfall in the present climate with the better prediction skills than existing statistical and operational numerical models. Using the PHRaMM, we evaluated the impacts of TC intensity and environmental moisture increase on TC rainfall change in a future climate. An increased TC intensity causes TC rainfall to increase in the inner-core region but to decrease in the outer region, whereas an increased environmental moisture causes the TC rainfall to increase over the entire TC area. According to the both effects of increased TC intensity and environmental moisture, the PHRaMM projected that the WNP TC rainfall could increase by 4.61–8.51% in the inner-core region and by 17.96–20.91% over the entire TC area under the 2-K warming scenario. 

This study analyzes an ensemble of numerical simulations of a heavy rainfall event east of Taiwan on 9 June 2020. Heavy rainfall was produced by quasi-stationary back-building mesoscale convective systems (MCS) associated with a mei-yu front. Global model forecast skill was poor in location and intensity of rainfall. The mesoscale ensemble showed liminal conditions between heavy rainfall or little to no rainfall. The two most accurate and two least accurate ensemble members are selected for analysis via validation against radar-estimated rainfall observations. All members feature moist soundings with low levels of free convection (LFC) and sufficient instability for deep convection. We find that stronger gradients in 100-m θe and θv in the most accurate members associated with a near-surface frontal boundary focus the lifting mechanism for deep, moist convection and enhanced rainfall. As the simulations progress, stronger southerly winds in the least accurate members advect drier mid-level air into the region of interest and shift the near-surface boundary further north and west. Analysis of the verification ensemble mean analysis reveals a strong near-surface frontal boundary similarly positioned as in the most accurate members and dry air aloft more similar to that in the least accurate members, suggesting that the positioning of the frontal boundary is more critical to accurately reproducing rainfall patterns and intensity in this case. The analyses suggest that subtle details in the simulation of frontal boundaries and mesoscale flow structures can lead to bifurcations in producing extreme or almost no rainfall. Implications for improved probabilistic forecasts of heavy rainfall events will be discussed. 

The Threespine Stickleback is ancestrally a marine fish, but many marine populations breed in fresh water (i.e., are anadromous), facilitating their colonization of isolated freshwater habitats a few years after they form. Repeated adaptation to fresh water during at least 10 My and continuing today has led to Threespine Stickleback becoming a premier system to study rapid adaptation. Anadromous and freshwater stickleback breed in sympatry and may hybridize, resulting in introgression of freshwater-adaptive alleles into anadromous populations, where they are maintained at low frequencies as ancient standing genetic variation. Anadromous stickleback have accumulated hundreds of freshwater-adaptive alleles that are disbursed as few loci per marine individual and provide the basis for adaptation when they colonize fresh water. Recent whole-lake experiments in lakes around Cook Inlet, Alaska have revealed how astonishingly rapid and repeatable this process is, with the frequency of 40% of the identified freshwater-adaptive alleles increasing from negligible (∼1%) in the marine founder to ≥50% within ten generations in fresh water, and freshwater phenotypes evolving accordingly. These high rates of genomic and phenotypic evolution imply very intense directional selection on phenotypes of heterozygotes. Sexual recombination rapidly assembles freshwater-adaptive alleles that originated in different founders into multilocus freshwater haplotypes, and regions important for adaptation to freshwater have suppressed recombination that keeps advantageous alleles linked within large haploblocks. These large haploblocks are also older and appear to have accumulated linked advantageous mutations. The contemporary evolution of Threespine Stickleback has provided broadly applicable insights into the mechanisms that facilitate rapid adaptation.

 

Understanding the mechanisms leading to new traits or additional features in organisms is a fundamental goal of evolutionary biology. We show thatHOXDBregulatory changes have been used repeatedly in different fish genera to alter the length and number of the prominent dorsal spines used to classify stickleback species. InGasterosteus aculeatus(typically ‘three-spine sticklebacks’), a variantHOXDBallele is genetically linked to shortening an existing spine and adding an additional spine. InApeltes quadracus(typically ‘four-spine sticklebacks’), a variantHOXDBallele is associated with lengthening a spine and adding an additional spine in natural populations. The variant alleles alter the same non-coding enhancer region in theHOXDBlocus but do so by diverse mechanisms, including single-nucleotide polymorphisms, deletions and transposable element insertions. The independent regulatory changes are linked to anterior expansion or contraction ofHOXDBexpression. We propose that associated changes in spine lengths and numbers are partial identity transformations in a repeating skeletal series that forms major defensive structures in fish. Our findings support the long-standing hypothesis that naturalHoxgene variation underlies key patterning changes in wild populations and illustrate how different mutational mechanisms affecting the same region may produce opposite gene expression changes with similar phenotypic outcomes.