Contours or closed planar curves are common in many domains. For example, they appear as object boundaries in computer vision, isolines in meteorology, and the orbits of rotating machinery. In many cases when learning from contour data, planar rotations of the input will result in correspondingly rotated outputs. It is therefore desirable that deep learning models be rotationally equivariant. In addition, contours are typically represented as an ordered sequence of edge points, where the choice of starting point is arbitrary. It is therefore also desirable for deep learning methods to be equivariant under cyclic shifts. We present RotaTouille, a deep learning framework for learning from contour data that achieves both rotation and cyclic shift equivariance through complex-valued circular convolution. We further introduce and characterize equivariant non-linearities, coarsening layers, and global pooling layers to obtain invariant representations for downstream tasks. Finally, we demonstrate the effectiveness of RotaTouille through experiments in shape classification, reconstruction, and contour regression.
Wildfire risk prediction remains a critical yet challenging task due to the complex interactions among fuel conditions, meteorology, topography, and human activity. Despite growing interest in data-driven approaches, publicly available benchmark datasets that support long-term temporal modeling, large-scale spatial coverage, and multimodal drivers remain scarce. To address this gap, we present a 25-year, daily-resolution wildfire dataset covering 240 million hectares across British Columbia and surrounding regions. The dataset includes 38 covariates, encompassing active fire detections, weather variables, fuel conditions, terrain features, and anthropogenic factors. Using this benchmark, we evaluate a diverse set of time-series forecasting models, including CNN-based, linear-based, Transformer-based, and Mamba-based architectures. We also investigate effectiveness of position embedding and the relative importance of different fire-driving factors. The dataset and the corresponding code can be found at https://github.com/SynUW/mmFire
Understanding how greenhouse gases and pollutants move through the atmosphere is essential for predicting and mitigating their effects. We present a novel atmospheric dispersion and footprint model: the Boundary Layer Dispersion and Footprint Model (BLDFM), which solves the three-dimensional steady-state advection-diffusion equation in Eulerian form using a numerical approach based on the Fourier method, the linear shooting method and the exponential integrator method. The model is designed to be flexible and can be used for a wide range of applications, including climate impact studies, industrial emissions monitoring, agricultural planning and spatial flux attribution. We validate the model using an analytical test case. The numerical results show excellent agreement with the analytical solution. We also compare the model with the Kormann and Meixner (Boundary-Layer Meteorology, 2001) footprint model (FKM), and the results show overall good agreement but some differences in the fetch of the footprints, which are attributed to the neglect of streamwise turbulent mixing in the FKM model. Our results demonstrate the potential of the BLDFM model as a useful tool for atmospheric scientists, biogeochemists, ecologists, and engineers.
The effects on the bistatic echo width of an elliptical cylinder due to a perfect magnetic material are reported in this article. The configuration is analyzed using the separation of variables method and Mathieu functions. In this approach, the structural geometry is illuminated by an electromagnetic field. Radial and angular Mathieu functions have been used in the formulation. Notably, the maxima of the scattered elliptic transfer electric mode ($θ= 180^{\circ}$) are much higher than those of the scattered transfer magnetic mode, comparable to terms $θ= 120^{\circ}$ and $θ= 240^{\circ}$, respectively. It can be observed that an increase in the in-plane radial component leads to the linearity principle for the transfer electric mode, while non-linear behavior is investigated for the elliptic transfer magnetic mode. Therefore, the unidirectional bistatic echo width is subject to non-directional behavior. These analogous results may have applications in the fields of optics, meteorology, acoustics, radio astronomy, collision physics, and other disciplines where wave scattering phenomena play a crucial role. Furthermore, the findings of this study contribute to the fundamental understanding of electromagnetic interactions with complex geometries and materials
The COVID-19 pandemic lockdown provided an unprecedented opportunity to examine changes in India’s air quality following abrupt reductions in anthropogenic emissions, particularly from transportation, industry, and construction. While many studies reported substantial pollution declines during the lockdown, most focused exclusively on this period, neglecting the subsequent ‘unlock’ phase, the influence of transboundary pollution, and the need to distinguish between emission-driven and meteorology-driven changes in PM _2.5 . Our study addresses these gaps by isolating the contributions of meteorological variability and activity restrictions on PM _2.5 across the entire lockdown and unlock phases (February 24-June 30, 2020) using a high-resolution modelling framework and satellite-derived PM _2.5 data. Through our WRF-Chem modeling study, we found that PM _2.5 concentrations decreased by 29% post-lockdown, compared to a 21% decline over the same period in preceding years, with satellite observations showing similar reductions of 31% and 22%, respectively. However, only an additional 8–9% reduction in 2020, beyond the typical interannual variability, was directly attributable to emission controls, while meteorological factors largely influenced the overall decline. The most pronounced PM _2.5 decline occurred in the Indo-Gangetic Plain during the unlock phase. Despite the initial improvements, restrictions on transportation, industry, and construction alone were insufficient to bring PM _2.5 levels below the National Ambient Air Quality Standards. A key finding is that persistent emissions from the residential sector, which remained largely unaffected during the lockdown, significantly limited the overall reduction in PM _2.5 . Without targeted interventions to address household emissions, such as promoting cleaner fuels and improving waste management to prevent garbage burning, India will struggle to achieve sustained air quality improvements. Our results emphasize the urgent need for integrated, regionally tailored, long-term strategies that address all major pollution sources to ensure lasting reductions in PM _2.5 levels. Implementing comprehensive measures can significantly improve India’s air quality, ensuring a healthier and more sustainable environment.
This study investigates the temporal variation and prediction of fine particulate matter (PM _2.5 ) concentrations in Ho Chi Minh City (HCM City), a major Vietnamese metropolis with a tropical monsoon climate, from 2018 to 2023. The results indicated no statistically significant differences in the annual average PM _2.5 concentrations throughout the study period, with values ranging from 21.7 to 26.5 μg m ^−3 . However, concentrations were consistently higher during the dry season (November to April) (mean ± SD: 27.4 ± 7.3 μg m ^−3 ) compared to the rainy season (May to November) (mean ± SD: 21.5 ± 6.2 μg m ^−3 ). PM _2.5 concentrations were strongly negatively correlated with meteorological parameters such as rainfall intensity, ambient air temperature, and wind speed, suggesting removal via wet deposition and enhanced dispersion under stronger winds and higher air temperatures. A bidirectional long short-term memory network with an attention mechanism (BiLSTM+Attention) was proposed to predict PM _2.5 concentrations, incorporating auxiliary variables such as meteorological parameters and the leaf area index from the most recent preceding hours. The model’s best performance was achieved when including both auxiliary variables and PM _2.5 concentrations from the previous 24 h, yielding a coefficient of determination (R ^2 ) of 0.944, a mean absolute error of 2.142 μg m ^−3 , and a root mean square error of 2.957 μg m ^−3 . Multi-horizon forecasting was also conducted to evaluate the model’s applicability, revealing a decline in prediction accuracy as the forecast horizon increased. SHAP (SHapley Additive exPlanations) was employed to evaluate the contribution of input variables to the model’s outputs, showing that PM _2.5 concentrations from prior hours (e.g, less than 4 h) were the most influential predictors. This study offers new insights into PM _2.5 pollution in HCM City and highlights the potential of advanced deep learning techniques for air quality prediction in tropical monsoon urban environments.
The levels of amylose and amylopectin in foxtail millet are important factors that influence grain quality. The application of organic fertilizers can affect the ratio of amylose and amylopectin components. These components are typically determined using chemical analysis methods, which are difficult to apply on a large scale for nutrient deficiency diagnosis and do not meet the original intention of precise agricultural development. This study set up five different gradient treatments for organic fertilizer (sheep manure) application. Hyperspectral imaging combined with chemometrics was employed to achieve rapid and non-destructive detection of the content of amylose and amylopectin in foxtail millet flour. The aim of this study was to determine the optimal dosage of organic fertilizers for application. Spectral data preprocessing used multiplicative scatter correction (MSC), and the combined algorithm of competitive adaptive reweighted sampling (CARS), random frog (RF), and iterated retaining informative variables (IRIVs) was employed for key band extraction. Partial least squares regression (PLSR) was then used to establish the prediction model and regression equation, which was used to visualize the two components. Results demonstrated that the key band extraction combined algorithm effectively reduced data dimension without compromising the accuracy of the prediction model. The prediction model for amylose using MSC–RF–IRIV–PLSR exhibited good performance, with the correlation coefficient (R) and root mean square error (RMSE) predicted to be 0.73 and 1.23 g/(100 g), respectively. Similarly, the prediction model for amylopectin using MSC–CARS–IRIV–PLSR also demonstrated good performance, with the R and RMSE values predicted to be 0.59 and 7.34 g/(100 g), respectively. The results of visualization and physicochemical determination showed that the amount of amylopectin accumulation was highest, and the amount of amylose was lowest, under the application of 22.5 t/ha of organic fertilizer. The experimental results offer valuable insights for the rapid detection of nutritional components in foxtail millet, serving as a basis for further research.
<p>Global navigation satellite system (GNSS) microwave signals are nearly unaffected by clouds but are delayed as they travel the troposphere. The hydrostatic delay accounts for approximately 90 % of the total delay and can be modelled well as a function of temperature, pressure, and humidity. On the other hand, the wet delay is highly variable in space and time, making it difficult to model accurately. A zenith wet delay (ZWD) can be estimated as part of the GNSS positioning adjustment and is proportional to the specific humidity in the atmospheric boundary layer (ABL). While its average term can describe mesoscale events, its small-scale component is associated with turbulent processes in the ABL and is the focus of the present contribution. We introduce a new filtering and estimation strategy to analyse small-scale ZWD variations, addressing questions related to daily or periodic variations in some turbulent parameters and to the dependence of these parameters on climate zones. Five GNSS stations were selected for case studies, revealing promising specific daily and seasonal patterns depending on the estimated turbulence at the GNSS station (buoyancy or shear). This research lays the groundwork for more accurate models and prediction strategies for integrated water vapour, WV (and potentially liquid water clouds), turbulence. It has far-reaching applications, from nowcasting uncertainty assessments to the stochastic modelling for very large baseline interferometry or GNSS.</p>
Tesfaye Akafu, Diriba Korecha, Weyessa Garedew
et al.
Agriculture is a crucial sector in Western Ethiopia that provides livelihoods to many people. However, climate change has brought about extreme weather conditions which pose a challenge to the agricultural sector. To combat the negative effects of climate change, adaptation strategies should be designed using historical and projected climate change patterns. This study aims to analyze spatiotemporal trends and variability in temperature and rainfall in the West Wallaga Zone, Western Ethiopia. The analysis used rainfall data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and satellite-based temperature data from the Nederlands Meteorologisch Instituut (Koninklijk Netherlands Meteorological Institute [KNMI]) covering a period from 1981 to 2022. The coefficient of variation (CV), rainfall anomaly index (RAI), and precipitation concentration index (PCI) were used to determine rainfall variability. The Mann–Kendall (MK) trend test and Sen’s slope methods were used to examine the existence of trends and slope magnitudes of temperature and rainfall. The result revealed a statistically significant increasing trend in annual temperatures. The mean maximum temperature showed higher seasonal variability than the mean minimum temperature for the past four decades. Annual, Bega, Belg, and Kiremt seasons rainfall increases with the trend being significant for all seasons except Kiremt. Spatial and temporal variations in rainfall were detected on both annual and seasonal timescales. Negative and positive rainfall anomalies each accounted for 50% of annual observations, with the highest seasonal negative anomalies occurring during the Belg. The PCI was highly irregular in Belg and uniform in the Kiremt season. Overall, the West Wallaga Zone has experienced moderate rainfall variability and significant warming over the past four decades. These findings provide important evidence to inform targeted adaptation strategies that can strengthen agricultural resilience under a changing climate.
Extreme value analysis (EVA) is a statistical method that studies the properties of extreme values of datasets, crucial for fields like engineering, meteorology, finance, insurance, and environmental science. EVA models extreme events using distributions such as Fréchet, Weibull, or Gumbel, aiding in risk prediction and management. This review explores EVA's application to nanoscale biological systems. Traditionally, biological research focuses on average values from repeated experiments. However, EVA offers insights into molecular mechanisms by examining extreme data points. We introduce EVA's concepts with simulations and review its use in studying motor protein movements within cells, highlighting the importance of in vivo analysis due to the complex intracellular environment. We suggest EVA as a tool for extracting motor proteins' physical properties in vivo and discuss its potential in other biological systems. While there have been only a few applications of EVA to biological systems, it holds promise for uncovering hidden properties in extreme data, promoting its broader application in life sciences.
Remote sensing images are highly valued for their ability to address complex real-world issues such as risk management, security, and meteorology. However, manually captioning these images is challenging and requires specialized knowledge across various domains. This letter presents an approach for automatically describing (captioning) remote sensing images. We propose a novel encoder-decoder setup that deploys a Text Graph Convolutional Network (TextGCN) and multi-layer LSTMs. The embeddings generated by TextGCN enhance the decoder's understanding by capturing the semantic relationships among words at both the sentence and corpus levels. Furthermore, we advance our approach with a comparison-based beam search method to ensure fairness in the search strategy for generating the final caption. We present an extensive evaluation of our approach against various other state-of-the-art encoder-decoder frameworks. We evaluated our method across three datasets using seven metrics: BLEU-1 to BLEU-4, METEOR, ROUGE-L, and CIDEr. The results demonstrate that our approach significantly outperforms other state-of-the-art encoder-decoder methods.
The Qinghai-Xizang Plateau （QXP） is an important herbage producing area， ecological barrier and water conservation area， the vegetation ecological process on which can directly affect the changes of China and even East Asia.With global warming， the phenological period of vegetation on the QXP is constantly changing， affecting climate and ecosystem through carbon cycle and hydrothermal cycle， etc.The study of phenological change and its influencing factors has become a key issue， and the construction of models that can realize future phenological prediction is of great scientific significance.In this paper， based on the Normalized Difference Vegetation Index acquired by satellites during 2000 -2020 （MODIS NDVI）， the dynamic threshold method was used to extract the start of growing season （SOS） on the QXP， and its spatiotemporal variation was analyzed in combination with vegetation types， so as to construct multiple phenological models of SOS， air temperature and soil moisture， exploring the hydrothermal conditions required for different regions and types of vegetation to start growing.The results showed that： （1） From 2000 to 2020， the overall SOS advance trend of the QXP was most significant in the eastern part of the region， where the SOS advance rate exceeded 10 d·（10a）-1.Coniferous forests， scrub， meadows， and alpine vegetation cover areas had a high percentage of SOS advance， and grasslands had about 50 % of slightly delayed areas.（2） The eastern and northern regions of the QXP showed an obvious warming and humidification trend.The average annual temperature rise rate was about 0.36 ℃·（10a）-1， and the average annual soil moisture increase rate was about 3.8×10-4 m3·m-3 （p<0.01）.（3） The parameters of the four phenological models showed that the vegetation growth in the eastern and southern QXP required higher hydrothermal conditions.The main controlling factor for vegetation SOS in the south was air temperature， while in the north it was soil moisture.The temperature and soil moisture thresholds and main controlling factor of different vegetation types were also closely related to their spatial distribution locations.（4） The cumulative temperature and cumulative soil moisture threshold model established in this paper has the best simulation effect for the main vegetation types （grassland， meadow and alpine vegetation） on the QXP， and the root-mean-square error is only about 8 days， which has reference significance for the future SOS prediction and the interaction mechanism between phenology and climate on the QXP.
Hamid Boubekraoui, Yazid Maouni, Abdelilah Ghallab
et al.
Formulating effective policies to address or mitigate deforestation requires a comprehensive understanding of the contributing factors. This study examines the drivers of deforestation from 2001 to 2020 in the Tangier-Tetouan-Al Hoceima (TTA) region, a northern Moroccan area distinguished by the country’s highest deforestation rate. Through an extensive review of existing literature and employing Geist and Lambin’s deforestation framework, we identified five key causes: infrastructure extension, agricultural expansion, logging, wildfires as direct causes, and demographic factors as an indirect cause. Data on deforestation and its contributing factors were sourced from diverse databases, including Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), World Population, Forest Proximate People (FPP), and National Forest Inventory (NFI) datasets. Pixel-level analysis of GFC data indicated that wildfires are the primary driver of deforestation in the region, accounting for 35.2%, followed by agricultural expansion (30.6%), logging (13.2%), and infrastructure extension (10.1%). The remaining 10.9% of losses were attributed to other disturbances, such as illegal extraction, pests, and dieback. Spatial patterns were further analyzed through Exploratory Spatial Data Analysis (ESDA) methods at a 1 km ^2 gridded scale, revealing strong clustering for all studied factors. Spatial relationships were explored using the bivariate local Moran’s index, which highlighted the highest spatial dependence between deforestation and fires (I = 0.21). Correlations between deforestation and other factors, including agricultural expansion, logging, infrastructure extension, and demographic pressure, were assessed at 0.18, 0.17, 0.08, and 0.05, respectively. Landscape pressures (LSP), encompassing deforestation, agricultural expansion, fires, infrastructure extension, and demographic pressure, were analyzed using the local Geary index, revealing a positive correlation in approximately 59% of spatial units. Last, a composite map of LSP clusters and an explanatory diagram illustrating dominant patterns in the TTA region were generated based on the results from local Geary’s multivariate and local Moran’s univariate tests.
Abstract The Hunga Tonga‐Hunga Ha'apai volcanic eruption on 15 January 2022 had a significant impact on the ionosphere‐thermosphere system, resulting in large‐scale ionospheric irregularities with longitudinal and latitudinal asymmetries. Multiple instruments recorded these irregularities, indicating the propagation of a westward wave at an average velocity of 354 ± 8 m/s, which led to plasma irregularities of 0.2 TECu/min. Conversely, an eastward‐propagating wave was detected on the Pacific's east coast, traveling at a speed of 348 ± 6 m/s, with a corresponding decrease in plasma fluctuations to 0.1 TECu/min. In Asia, noticeable plasma irregularities appeared within a few hours after the eruption, and the maximum speed exceeded 1,100 m/s, which cannot be explained by the acoustic wave model. There was also a significant latitudinal asymmetry of ionospheric disturbances in the Asian‐Oceania sector, with the plasma density around Oceania depleted by 2–3 orders of magnitude within the altitudes of ∼150–575 km, while the ion density over Asia was enhanced by 1–2 orders of magnitude, and was uplifted ∼50 km. The plasma temperature was proportional to ion density, indicating the ion temperature reduced ∼500 K and increased 100–200 K around Oceania and Asia, respectively. The equatorial electric field, vertical E × B drifts and thermospheric O/N2 density ratio also fluctuated significantly following the eruption, indicating the redistribution of charged particles due to the magnetic field mapping effect, which was the main contributor to the asymmetries observed.
Samuel Elias Kayamo, Christian Troost, Habtamu Yismaw
et al.
Among many other options, seasonal weather forecasts and the use of cultivars that are better adapted to local climate and climate variability have been discussed as two potential supporting measures to assist farmer adaptation to climatic variability and change. In this article, we evaluate the potential benefit of combining these two measures, i.e., choosing specific crop varieties based on seasonal forecasts – focusing on the Central Rift Valley in Ethiopia. We base our value of information analysis on the available records of field trial data for publicly released crop varieties. We find that experimental evidence must be extended and improved in order to provide reliable evidence of yield performance differences between crop varieties, which is an essential prerequisite for exploiting forecast information. Classification of cumulative seasonal rainfall based on the modified Rainfall Anomaly Index provides a sharper distinction than using the standard tercile-based approach employed in the Ethiopian seasonal forecast communication. Even with a fairly optimistic interpretation of the evidence with respect to exploitable yield differences, we find only modest benefits of seasonal forecasts at realistic forecast accuracy for the region. Given the empirical limitations when assembling long-run yield data, the presented results have to be understood as a first approximation. Apart from sufficiently accurate forecasts, success of forecast-based cultivar choice will depend on (i) more reliable evidence on performance differences between crop varieties under different weather conditions, and (ii) changes in the current seed breeding and distribution system in developing countries, because the full potential of high-accuracy seasonal forecast information could only be tapped, if forecast-matching cultivars are being made available to farmers in time.
P. J. Stoll, R. G. Graversen, R. G. Graversen
et al.
<p>The atmosphere transports energy polewards by circulation cells and eddies. To the present day, there has been a knowledge gap regarding the preferred spatial scales and physical mechanisms of eddy energy transport. To fill the gap, we separate the meridional atmospheric energy transport in the ERA5 reanalysis by spatial scales and into quasi-stationary and transient flow patterns and latent and dry components.</p>
<p>Baroclinic instability is the major instability mechanism in the transient synoptic scales and is responsible for forming cyclones, anticyclones, and small-scale Rossby waves. At the planetary scales, circulation patterns are often induced by other mechanisms such as flow interaction with orography and land–sea heating contrasts. However, a separation between circulation patterns at the synoptic and planetary scales has yet to be established.
We find that both baroclinically induced and transient energy transport is predominantly associated with eddies at wavelengths between 2000 and 8000 km.
The maxima in both types of transport occur at wavelengths around 5000 km, in good agreement with linear baroclinic theory. Since these results are independent of latitude, we adapt the scale separation of the energy transport to be based on the wavelength instead of the previously used wavenumber. We define the synoptic transport by the wavelength band between 2000 and 8000 km.</p>
<p>We analyse the annual and seasonal mean in the energy transport components and their inter-annual variability. The scale-separated transport components are fairly similar in both hemispheres.
Transport by synoptic waves is the largest contributor to extra-tropical energy and moisture transport, mainly of a transient character, and is influenced little by seasonality. In contrast, transport by planetary waves depends highly on the season and has two distinct characteristics. (1) In the extra-tropical winter, planetary waves are important due to a large transport of dry energy. This planetary transport features the largest inter-annual variability of all components and is mainly quasi-stationary in the Northern Hemisphere but transient in its southern counterpart. (2) In the sub-tropical summer, quasi-stationary planetary waves are the most important transport component, mainly due to moisture transport, presumably associated with monsoons.
In contrast to transport by planetary and synoptic waves, only a negligible amount of energy is transported by mesoscale eddies (<span class="inline-formula"><</span> 2000 km).</p>
Fraser King, George Duffy, Christopher G. Fletcher
Abstract Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size, and distribution that contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the vertically pointing X-band radar (VertiX) instrument in Egbert, Ontario, Canada, are compared with in situ surface snow accumulation measurements from January to March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and ERA5 atmospheric temperature estimates to derive a surface snow accumulation regression model. Using event-based training–testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-min intervals with a low mean-square error of approximately 1.8 × 10−3 mm2 when compared with collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Ze–S relationships) that were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning–based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.
Transient recurring phenomena are ubiquitous in many scientific fields like neuroscience and meteorology. Time inhomogenous Vector Autoregressive Models (VAR) may be used to characterize peri-event system dynamics associated with such phenomena, and can be learned by exploiting multi-dimensional data gathering samples of the evolution of the system in multiple time windows comprising, each associated with one occurrence of the transient phenomenon, that we will call "trial". However, optimal VAR model order selection methods, commonly relying on the Akaike or Bayesian Information Criteria (AIC/BIC), are typically not designed for multi-trial data. Here we derive the BIC methods for multi-trial ensemble data which are gathered after the detection of the events. We show using simulated bivariate AR models that the multi-trial BIC is able to recover the real model order. We also demonstrate with simulated transient events and real data that the multi-trial BIC is able to estimate a sufficiently small model order for dynamic system modeling.
Cooling a mechanical mode to its motional ground state opens up avenues for both scientific and technological advancements in the field of quantum meteorology and information processing. We propose a multi-parameter optimization scheme for ground-state cooling of a mechanical mode using quantum dots. Applying the master equation approach, we formulate the optimization scheme over a broad range of system parameters including detunings, decay rates, pumping rates, and coupling strengths. We implement the optimization scheme on two major types of semiconductor quantum dot systems: colloidal and epitaxial quantum dots. These systems span a broad range of mechanical mode frequencies, coupling rates, and decay rates. Our optimization scheme lowers the steady-state phonon number in all cases by several orders of magnitude. We also calculate the net cooling rate by estimating the phonon decay rate and show that the optimized system parameters also result in efficient cooling. The proposed optimization scheme can be readily extended to other driven systems coupled to a mechanical mode.