```python
import numpy as np
import matplotlib.pyplot as plt
from pysteps import io, motion, nowcasts, verification, utils
from pysteps.visualization import plot_precip_field
from sklearn.metrics import roc_curve
from pysteps.utils import transformation

# Load the radar data
data, metadata = io.import_mch_gif("path_to_radar_data.gif")

# Upscale data to 2 km resolution
precip_field = utils.dimension.aggregate_fields_space(data, metadata, agg_size=2)

# Convert to rain rate
rain_rate = utils.conversion.to_rainrate(precip_field, metadata)

# Log-transform and handle missing values
log_rain_rate, metadata = transformation.dB_transform(rain_rate, metadata, threshold=0.1, zerovalue=-15.0)

# Estimate motion field
oflow_method = motion.get_method("LK")
velocity_field = oflow_method(log_rain_rate)

# Perform ensemble nowcast
nowcast_method = nowcasts.get_method("steps")
ensemble_precip_fields = nowcast_method(log_rain_rate, velocity_field, timesteps=20, num_ens_members=5, 
                                        noise_method='nonparametric', ar_order=2, conditional=True)

# Back-transform to rain rates
ensemble_precip_fields = transformation.dB_transform(ensemble_precip_fields, inverse=True)

# Plot some of the realizations
fig, axs = plt.subplots(1, 3, figsize=(15, 5))
for i, ax in enumerate(axs.flat):
    plot_precip_field(ensemble_precip_fields[i, :, :], ax=ax)
plt.show()

# Verification
# Convert the ensemble nowcasts to binary forecasts for a specific threshold
threshold = 1  # mm/h
binary_forecasts = ensemble_precip_fields >= threshold

# Assume we have corresponding observations to compare against
# For demonstration, let's create dummy observations
observations = np.random.choice([0, 1], ensemble_precip_fields.shape)

# Compute ROC curve
fpr, tpr, thresholds = roc_curve(observations.flatten(), binary_forecasts.mean(axis=0).flatten())

# Plot ROC curve
plt.figure()
plt.plot(fpr, tpr, label='ROC Curve')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend()
plt.show()

# Reliability diagrams and rank histograms can be plotted using the verification module
# This requires actual implementation of the functions to compute and plot these diagrams
# For demonstration, placeholders for function calls are provided

# Reliability diagram
reliability_diagram = verification.reliability_diagram(observations, binary_forecasts)
# Plot reliability diagram (Assuming the function returns necessary data for plotting)
# plt.plot(...)

# Rank histogram
rank_histogram = verification.rank_histogram(observations, ensemble_precip_fields)
# Plot rank histogram (Assuming the function returns necessary data for plotting)
# plt.bar(...)
```